benchmarking.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmarking Tool-using Agentic Approach\n",
    "\n",
    "* After exploring a variety of possible benchmarks, I decided to focus on SemScore, which is a semantic similarity metric. \n",
    "* The idea is to evaluate how well the agent can answer questions that are syntactically and semantically similar to the reference answers.\n",
    "* It uses cosine similarity of embedding vectors to measure the semantic similarity between the predicted answer and the reference answer.\n",
    "* It is a good metric for evaluating the quality of the agent's answers, but it does not take into account the existence of multiple acceptable answers.\n",
    "* It also does not take into account the quality of the question, which is as important as the quality of the answer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from transformers.agents import agent_types\n",
    "from tqdm.notebook import tqdm\n",
    "import logging\n",
    "from IPython.display import Markdown\n",
    "from semscore import EmbeddingModelWrapper\n",
    "from statistics import mean\n",
    "from agent import get_agent\n",
    "from openai import OpenAI\n",
    "from prompts import (\n",
    "    SUCCINCT_SQUAD_REACT_CODE_SYSTEM_PROMPT,\n",
    "    FOCUSED_SQUAD_REACT_CODE_SYSTEM_PROMPT,\n",
    "    DEFAULT_SQUAD_REACT_CODE_SYSTEM_PROMPT,\n",
    ")\n",
    "import re\n",
    "from string import punctuation\n",
    "from nltk.corpus import stopwords\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv()  # Load OPENAI_API_KEY from .env (not included in repo)\n",
    "\n",
    "SAMPLES_DIR = \"samples\"\n",
    "BENCHMARKS_DIR = \"benchmarks\"\n",
    "STOP_WORDS = set(stopwords.words(\"english\"))\n",
    "\n",
    "\n",
    "def display_text_df(df):\n",
    "    display(\n",
    "        df.style.set_properties(**{\"white-space\": \"pre-wrap\"}).set_table_styles(\n",
    "            [\n",
    "                {\"selector\": \"th\", \"props\": [(\"text-align\", \"left\")]},\n",
    "                {\"selector\": \"td\", \"props\": [(\"text-align\", \"left\")]},\n",
    "            ]\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  text-align: left;\n",
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       "#T_debc1 td {\n",
       "  text-align: left;\n",
       "}\n",
       "#T_debc1_row0_col0, #T_debc1_row0_col1, #T_debc1_row0_col2, #T_debc1_row0_col3, #T_debc1_row1_col0, #T_debc1_row1_col1, #T_debc1_row1_col2, #T_debc1_row1_col3, #T_debc1_row2_col0, #T_debc1_row2_col1, #T_debc1_row2_col2, #T_debc1_row2_col3 {\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_debc1\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_debc1_level0_col0\" class=\"col_heading level0 col0\" >Title</th>\n",
       "      <th id=\"T_debc1_level0_col1\" class=\"col_heading level0 col1\" >Context</th>\n",
       "      <th id=\"T_debc1_level0_col2\" class=\"col_heading level0 col2\" >Question</th>\n",
       "      <th id=\"T_debc1_level0_col3\" class=\"col_heading level0 col3\" >Answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_debc1_row0_col0\" class=\"data row0 col0\" >University_of_Notre_Dame</td>\n",
       "      <td id=\"T_debc1_row0_col1\" class=\"data row0 col1\" >Architecturally, the school has a Catholic character. Atop the Main Building's gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.</td>\n",
       "      <td id=\"T_debc1_row0_col2\" class=\"data row0 col2\" >To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?</td>\n",
       "      <td id=\"T_debc1_row0_col3\" class=\"data row0 col3\" >Saint Bernadette Soubirous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_debc1_row1_col0\" class=\"data row1 col0\" >University_of_Notre_Dame</td>\n",
       "      <td id=\"T_debc1_row1_col1\" class=\"data row1 col1\" >Architecturally, the school has a Catholic character. Atop the Main Building's gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.</td>\n",
       "      <td id=\"T_debc1_row1_col2\" class=\"data row1 col2\" >What is in front of the Notre Dame Main Building?</td>\n",
       "      <td id=\"T_debc1_row1_col3\" class=\"data row1 col3\" >a copper statue of Christ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_debc1_row2_col0\" class=\"data row2 col0\" >University_of_Notre_Dame</td>\n",
       "      <td id=\"T_debc1_row2_col1\" class=\"data row2 col1\" >Architecturally, the school has a Catholic character. Atop the Main Building's gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.</td>\n",
       "      <td id=\"T_debc1_row2_col2\" class=\"data row2 col2\" >The Basilica of the Sacred heart at Notre Dame is beside to which structure?</td>\n",
       "      <td id=\"T_debc1_row2_col3\" class=\"data row2 col3\" >the Main Building</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x438e3dc40>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(87599, 4)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from data import get_data\n",
    "data = get_data(download=False)\n",
    "display_text_df(data.df.head(3))\n",
    "data.df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sample 100 random rows from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_e89a8\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_e89a8_level0_col0\" class=\"col_heading level0 col0\" >Title</th>\n",
       "      <th id=\"T_e89a8_level0_col1\" class=\"col_heading level0 col1\" >Context</th>\n",
       "      <th id=\"T_e89a8_level0_col2\" class=\"col_heading level0 col2\" >Question</th>\n",
       "      <th id=\"T_e89a8_level0_col3\" class=\"col_heading level0 col3\" >Answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_e89a8_row0_col0\" class=\"data row0 col0\" >Institute_of_technology</td>\n",
       "      <td id=\"T_e89a8_row0_col1\" class=\"data row0 col1\" >The world's first institution of technology or technical university with tertiary technical education is the Banská Akadémia in Banská Štiavnica, Slovakia, founded in 1735, Academy since December 13, 1762 established by queen Maria Theresa in order to train specialists of silver and gold mining and metallurgy in neighbourhood. Teaching started in 1764. Later the department of Mathematics, Mechanics and Hydraulics and department of Forestry were settled. University buildings are still at their place today and are used for teaching. University has launched the first book of electrotechnics in the world.</td>\n",
       "      <td id=\"T_e89a8_row0_col2\" class=\"data row0 col2\" >What year was the Banská Akadémia founded?</td>\n",
       "      <td id=\"T_e89a8_row0_col3\" class=\"data row0 col3\" >1735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e89a8_row1_col0\" class=\"data row1 col0\" >Film_speed</td>\n",
       "      <td id=\"T_e89a8_row1_col1\" class=\"data row1 col1\" >The standard specifies how speed ratings should be reported by the camera. If the noise-based speed (40:1) is higher than the saturation-based speed, the noise-based speed should be reported, rounded downwards to a standard value (e.g. 200, 250, 320, or 400). The rationale is that exposure according to the lower saturation-based speed would not result in a visibly better image. In addition, an exposure latitude can be specified, ranging from the saturation-based speed to the 10:1 noise-based speed. If the noise-based speed (40:1) is lower than the saturation-based speed, or undefined because of high noise, the saturation-based speed is specified, rounded upwards to a standard value, because using the noise-based speed would lead to overexposed images. The camera may also report the SOS-based speed (explicitly as being an SOS speed), rounded to the nearest standard speed rating.</td>\n",
       "      <td id=\"T_e89a8_row1_col2\" class=\"data row1 col2\" >What is another speed that can also be reported by the camera?</td>\n",
       "      <td id=\"T_e89a8_row1_col3\" class=\"data row1 col3\" >SOS-based speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e89a8_row2_col0\" class=\"data row2 col0\" >Sumer</td>\n",
       "      <td id=\"T_e89a8_row2_col1\" class=\"data row2 col1\" >The most impressive and famous of Sumerian buildings are the ziggurats, large layered platforms which supported temples. Sumerian cylinder seals also depict houses built from reeds not unlike those built by the Marsh Arabs of Southern Iraq until as recently as 400 CE. The Sumerians also developed the arch, which enabled them to develop a strong type of dome. They built this by constructing and linking several arches. Sumerian temples and palaces made use of more advanced materials and techniques,[citation needed] such as buttresses, recesses, half columns, and clay nails.</td>\n",
       "      <td id=\"T_e89a8_row2_col2\" class=\"data row2 col2\" >Where were the use of advanced materials and techniques on display in Sumer?</td>\n",
       "      <td id=\"T_e89a8_row2_col3\" class=\"data row2 col3\" >Sumerian temples and palaces</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e89a8_row3_col0\" class=\"data row3 col0\" >Ann_Arbor,_Michigan</td>\n",
       "      <td id=\"T_e89a8_row3_col1\" class=\"data row3 col1\" >Ann Arbor has a council-manager form of government. The City Council has 11 voting members: the mayor and 10 city council members. The mayor and city council members serve two-year terms: the mayor is elected every even-numbered year, while half of the city council members are up for election annually (five in even-numbered and five in odd-numbered years). Two council members are elected from each of the city's five wards. The mayor is elected citywide. The mayor is the presiding officer of the City Council and has the power to appoint all Council committee members as well as board and commission members, with the approval of the City Council. The current mayor of Ann Arbor is Christopher Taylor, a Democrat who was elected as mayor in 2014. Day-to-day city operations are managed by a city administrator chosen by the city council.</td>\n",
       "      <td id=\"T_e89a8_row3_col2\" class=\"data row3 col2\" >Who is elected every even numbered year?</td>\n",
       "      <td id=\"T_e89a8_row3_col3\" class=\"data row3 col3\" >mayor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e89a8_row4_col0\" class=\"data row4 col0\" >John_von_Neumann</td>\n",
       "      <td id=\"T_e89a8_row4_col1\" class=\"data row4 col1\" >Shortly before his death, when he was already quite ill, von Neumann headed the United States government's top secret ICBM committee, and it would sometimes meet in his home. Its purpose was to decide on the feasibility of building an ICBM large enough to carry a thermonuclear weapon. Von Neumann had long argued that while the technical obstacles were sizable, they could be overcome in time. The SM-65 Atlas passed its first fully functional test in 1959, two years after his death. The feasibility of an ICBM owed as much to improved, smaller warheads as it did to developments in rocketry, and his understanding of the former made his advice invaluable.</td>\n",
       "      <td id=\"T_e89a8_row4_col2\" class=\"data row4 col2\" >What was the purpose of top secret ICBM committee?</td>\n",
       "      <td id=\"T_e89a8_row4_col3\" class=\"data row4 col3\" >decide on the feasibility of building an ICBM large enough to carry a thermonuclear weapon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "(100, 4)"
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     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "np.random.seed(42)\n",
    "# Select 10 random rows from data.df\n",
    "dfSample = data.df.sample(n=100)\n",
    "display_text_df(dfSample.head())\n",
    "dfSample.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthesize Unambiguous Questions\n",
    "\n",
    "* Because the solution is Closed Generative QA, the raw questions in the dataset may result in unreasonable standards in the benchmark due to their ambiguity.\n",
    "* Therefore, we need to synthesize unambiguous questions.\n",
    "* For this, we will use GPT-4o-mini and a simple prompt, one-shot prompt to synthesize the questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table id=\"T_2245b\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_2245b_level0_col0\" class=\"col_heading level0 col0\" >Title</th>\n",
       "      <th id=\"T_2245b_level0_col1\" class=\"col_heading level0 col1\" >Context</th>\n",
       "      <th id=\"T_2245b_level0_col2\" class=\"col_heading level0 col2\" >Question</th>\n",
       "      <th id=\"T_2245b_level0_col3\" class=\"col_heading level0 col3\" >Answer</th>\n",
       "      <th id=\"T_2245b_level0_col4\" class=\"col_heading level0 col4\" >Synthesized Question</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_2245b_row0_col0\" class=\"data row0 col0\" >Institute_of_technology</td>\n",
       "      <td id=\"T_2245b_row0_col1\" class=\"data row0 col1\" >The world's first institution of technology or technical university with tertiary technical education is the Banská Akadémia in Banská Štiavnica, Slovakia, founded in 1735, Academy since December 13, 1762 established by queen Maria Theresa in order to train specialists of silver and gold mining and metallurgy in neighbourhood. Teaching started in 1764. Later the department of Mathematics, Mechanics and Hydraulics and department of Forestry were settled. University buildings are still at their place today and are used for teaching. University has launched the first book of electrotechnics in the world.</td>\n",
       "      <td id=\"T_2245b_row0_col2\" class=\"data row0 col2\" >What year was the Banská Akadémia founded?</td>\n",
       "      <td id=\"T_2245b_row0_col3\" class=\"data row0 col3\" >1735</td>\n",
       "      <td id=\"T_2245b_row0_col4\" class=\"data row0 col4\" >What year was the Banská Akadémia, the world's first institution of technology, founded in Banská Štiavnica, Slovakia?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2245b_row1_col0\" class=\"data row1 col0\" >Film_speed</td>\n",
       "      <td id=\"T_2245b_row1_col1\" class=\"data row1 col1\" >The standard specifies how speed ratings should be reported by the camera. If the noise-based speed (40:1) is higher than the saturation-based speed, the noise-based speed should be reported, rounded downwards to a standard value (e.g. 200, 250, 320, or 400). The rationale is that exposure according to the lower saturation-based speed would not result in a visibly better image. In addition, an exposure latitude can be specified, ranging from the saturation-based speed to the 10:1 noise-based speed. If the noise-based speed (40:1) is lower than the saturation-based speed, or undefined because of high noise, the saturation-based speed is specified, rounded upwards to a standard value, because using the noise-based speed would lead to overexposed images. The camera may also report the SOS-based speed (explicitly as being an SOS speed), rounded to the nearest standard speed rating.</td>\n",
       "      <td id=\"T_2245b_row1_col2\" class=\"data row1 col2\" >What is another speed that can also be reported by the camera?</td>\n",
       "      <td id=\"T_2245b_row1_col3\" class=\"data row1 col3\" >SOS-based speed</td>\n",
       "      <td id=\"T_2245b_row1_col4\" class=\"data row1 col4\" >What is another speed rating that can also be reported by the camera in addition to the noise-based and saturation-based speeds?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2245b_row2_col0\" class=\"data row2 col0\" >Sumer</td>\n",
       "      <td id=\"T_2245b_row2_col1\" class=\"data row2 col1\" >The most impressive and famous of Sumerian buildings are the ziggurats, large layered platforms which supported temples. Sumerian cylinder seals also depict houses built from reeds not unlike those built by the Marsh Arabs of Southern Iraq until as recently as 400 CE. The Sumerians also developed the arch, which enabled them to develop a strong type of dome. They built this by constructing and linking several arches. Sumerian temples and palaces made use of more advanced materials and techniques,[citation needed] such as buttresses, recesses, half columns, and clay nails.</td>\n",
       "      <td id=\"T_2245b_row2_col2\" class=\"data row2 col2\" >Where were the use of advanced materials and techniques on display in Sumer?</td>\n",
       "      <td id=\"T_2245b_row2_col3\" class=\"data row2 col3\" >Sumerian temples and palaces</td>\n",
       "      <td id=\"T_2245b_row2_col4\" class=\"data row2 col4\" >Where were the advanced materials and techniques, such as buttresses and arches, used in Sumerian temples and palaces on display?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use local gpt to synthesize questions with context\n",
    "\n",
    "synth_system_prompt = \"\"\"\n",
    "You are an expert at clarifying what questions are really asking for.\n",
    "\n",
    "You will be given a question, a title and context.\n",
    "Your task is come up with a new version of the question that resolves ambiguities \n",
    "by adding only and exactly the necessary details from the title and context \n",
    "in a way that clarifies the question without changing the meaning or intent of the question.\n",
    "\n",
    "For example: \n",
    "Question: Who does M fight with?\n",
    "Title: Spectre_(2015_film)\n",
    "Context: Bond and Swann return to London where they meet M, Bill Tanner, Q, and Moneypenny; they intend to arrest C and stop Nine Eyes from going online. Swann leaves Bond, telling him she cannot be part of a life involving espionage, and is subsequently kidnapped. On the way, the group is ambushed and Bond is kidnapped, but the rest still proceed with the plan. After Q succeeds in preventing the Nine Eyes from going online, a brief struggle between M and C ends with the latter falling to his death. Meanwhile, Bond is taken to the old MI6 building, which is scheduled for demolition, and frees himself. Moving throughout the ruined labyrinth, he encounters a disfigured Blofeld, who tells him that he has three minutes to escape the building before explosives are detonated or die trying to save Swann. Bond finds Swann and the two escape by boat as the building collapses. Bond shoots down Blofeld's helicopter, which crashes onto Westminster Bridge. As Blofeld crawls away from the wreckage, Bond confronts him but ultimately leaves him to be arrested by M. Bond leaves the bridge with Swann.\n",
    "Response: Who does M struggle with during the events of Spectre (2015)?\n",
    "\"\"\"\n",
    "\n",
    "synth_user_prompt = \"\"\"\n",
    "Question: {question}\n",
    "Title: {title}\n",
    "Context: {context}\n",
    "\"\"\"\n",
    "\n",
    "client = OpenAI()\n",
    "\n",
    "# if the samples file does not exist, synthesize the questions and save them\n",
    "if not os.path.exists(os.path.join(SAMPLES_DIR, f\"samples.pkl\")):\n",
    "    synth_answers = []\n",
    "    for title, context, question, answer in tqdm(dfSample.values):\n",
    "        completion = client.chat.completions.create(\n",
    "        model=\"gpt-4o-mini-2024-07-18\",\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": synth_system_prompt},\n",
    "            {\"role\": \"user\", \"content\": synth_user_prompt.format(question=question, title=title, context=context)}\n",
    "        ],\n",
    "        temperature=0.7,\n",
    "        )\n",
    "        synth_answers.append(completion.choices[0].message.content)\n",
    "\n",
    "    dfSample[\"Synthesized Question\"] = synth_answers\n",
    "\n",
    "    os.makedirs(SAMPLES_DIR, exist_ok=True)\n",
    "    dfSample.to_pickle(os.path.join(SAMPLES_DIR, f\"samples.pkl\"))    \n",
    "else:\n",
    "    # if the samples file exists, load it\n",
    "    dfSample = pd.read_pickle(os.path.join(SAMPLES_DIR, f\"samples.pkl\"))\n",
    "\n",
    "display_text_df(dfSample.head(3))\n",
    "dfSample.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmark the agent\n",
    "\n",
    "* First, let's test the agent on a single question to see how it performs, show its logs, and the final answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32;20;1m======== New task ========\u001b[0m\n",
      "\u001b[37;1mWhat year was the Banská Akadémia, the world's first institution of technology, founded in Banská Štiavnica, Slovakia?\u001b[0m\n",
      "\u001b[33;1m=== Agent thoughts:\u001b[0m\n",
      "\u001b[0mThought: I will use the squad_retriever tool to find information about the Banská Akadémia, specifically its founding year. I will phrase my query to include details about its significance as the world's first institution of technology located in Banská Štiavnica, Slovakia.\u001b[0m\n",
      "\u001b[33;1m>>> Agent is executing the code below:\u001b[0m\n",
      "\u001b[0m\u001b[38;5;7manswer\u001b[39m\u001b[38;5;7m \u001b[39m\u001b[38;5;109;01m=\u001b[39;00m\u001b[38;5;7m \u001b[39m\u001b[38;5;7msquad_retriever\u001b[39m\u001b[38;5;7m(\u001b[39m\u001b[38;5;7mquery\u001b[39m\u001b[38;5;109;01m=\u001b[39;00m\u001b[38;5;144m\"\u001b[39m\u001b[38;5;144mWhat year was the Banská Akadémia founded in Banská Štiavnica, Slovakia, known as the world\u001b[39m\u001b[38;5;144m'\u001b[39m\u001b[38;5;144ms first institution of technology?\u001b[39m\u001b[38;5;144m\"\u001b[39m\u001b[38;5;7m)\u001b[39m\n",
      "\u001b[38;5;109mprint\u001b[39m\u001b[38;5;7m(\u001b[39m\u001b[38;5;7manswer\u001b[39m\u001b[38;5;7m)\u001b[39m\u001b[0m\n",
      "\u001b[33;1m====\u001b[0m\n",
      "\u001b[33;1mPrint outputs:\u001b[0m\n",
      "\u001b[32;20m===Document===\n",
      "Title: Institute_of_technology\n",
      "Context: The world's first institution of technology or technical university with tertiary technical education is the Banská Akadémia in Banská Štiavnica, Slovakia, founded in 1735, Academy since December 13, 1762 established by queen Maria Theresa in order to train specialists of silver and gold mining and metallurgy in neighbourhood. Teaching started in 1764. Later the department of Mathematics, Mechanics and Hydraulics and department of Forestry were settled. University buildings are still at their place today and are used for teaching. University has launched the first book of electrotechnics in the world.\n",
      "Question: What year was the Banská Akadémia founded?\n",
      "Acceptable Answers:\n",
      "['1. 1735']\n",
      "Score: 0.8805255214632872\n",
      "===Document===\n",
      "Title: Institute_of_technology\n",
      "Context: The world's first institution of technology or technical university with tertiary technical education is the Banská Akadémia in Banská Štiavnica, Slovakia, founded in 1735, Academy since December 13, 1762 established by queen Maria Theresa in order to train specialists of silver and gold mining and metallurgy in neighbourhood. Teaching started in 1764. Later the department of Mathematics, Mechanics and Hydraulics and department of Forestry were settled. University buildings are still at their place today and are used for teaching. University has launched the first book of electrotechnics in the world.\n",
      "Question: What year did teaching start at the Banská Akadémia?\n",
      "Acceptable Answers:\n",
      "['1. 1764']\n",
      "Score: 0.8732076610524725\n",
      "\u001b[0m\n",
      "\u001b[33;1m=== Agent thoughts:\u001b[0m\n",
      "\u001b[0mThought: From the information retrieved, I learned that the Banská Akadémia was founded in the year 1735. I will now use this information to provide the final answer.\u001b[0m\n",
      "\u001b[33;1m>>> Agent is executing the code below:\u001b[0m\n",
      "\u001b[0m\u001b[38;5;7mfinal_answer\u001b[39m\u001b[38;5;7m(\u001b[39m\u001b[38;5;144m\"\u001b[39m\u001b[38;5;144mThe Banská Akadémia, the world\u001b[39m\u001b[38;5;144m'\u001b[39m\u001b[38;5;144ms first institution of technology, was founded in the year 1735.\u001b[39m\u001b[38;5;144m\"\u001b[39m\u001b[38;5;7m)\u001b[39m\u001b[0m\n",
      "\u001b[33;1m====\u001b[0m\n",
      "\u001b[33;1mPrint outputs:\u001b[0m\n",
      "\u001b[32;20m\u001b[0m\n",
      "\u001b[33;1mLast output from code snippet:\u001b[0m\n",
      "\u001b[32;20mThe Banská Akadémia, the world's first institution of technology, was founded in the year 1735.\u001b[0m\n",
      "\u001b[32;20;1mFinal answer:\u001b[0m\n",
      "\u001b[32;20mThe Banská Akadémia, the world's first institution of technology, was founded in the year 1735.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"The Banská Akadémia, the world's first institution of technology, was founded in the year 1735.\""
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prompt = \"What year was the Banská Akadémia, the world's first institution of technology, founded in Banská Štiavnica, Slovakia?\"\n",
    "agent = get_agent()\n",
    "agent.run(prompt, stream=False, reset=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the benchmark\n",
    "\n",
    "* We are using semantic similarity to evaluate the agent's answers against the reference answers.\n",
    "* During test runs, it became clear that the agent was being penalized for punctuation, stop words, and minor differences in case.\n",
    "* Therefore, we will clean the text of the expected and predicted answers before calculating the semantic similarity.\n",
    "\n",
    "### BenchmarkDesign Notes\n",
    "* One flaw of this approach is that it does not take into account the existence of multiple acceptable answers.\n",
    "* Another flaw is that the agent me be unfairly penalized for elaborating on the answer, while this benchmark focuses on only and exactly the one canonical answer given.\n",
    "* That said, semantic similarity strongly correlates with human judgement of answer quality, so it's a good proxy for evaluating the agent's answers.\n",
    "  * Source: https://arxiv.org/pdf/2401.17072"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    # Lowercase\n",
    "    text = text.lower()\n",
    "    # Remove punctuation\n",
    "    text = text.translate(str.maketrans(\"\", \"\", punctuation))\n",
    "    # Remove stop words\n",
    "    text = \" \".join([word for word in text.split() if word not in STOP_WORDS])\n",
    "    return text\n",
    "\n",
    "def benchmark_agent(agent, dfSample, name):\n",
    "    answers_ref, answers_pred = [], []        \n",
    "\n",
    "    # Suppress logging from the agent, which can be quite verbose\n",
    "    agent.logger.setLevel(logging.CRITICAL)\n",
    "\n",
    "    for title, context, question, answer, synthesized_question in tqdm(dfSample.values):\n",
    "        prompt = synthesized_question\n",
    "        answers_ref.append(answer)\n",
    "        final_answer = agent.run(prompt, stream=False, reset=True)\n",
    "        answers_pred.append(final_answer)\n",
    "\n",
    "    answers_ref = [str(answer) for answer in answers_ref]\n",
    "    answers_pred = [str(answer) for answer in answers_pred]\n",
    "\n",
    "    dfAnswers = dfSample.copy()\n",
    "    dfAnswers[\"Predicted Answer\"] = answers_pred\n",
    "\n",
    "    # Remove stop words and punctuation from answers\n",
    "    answers_ref = [clean_text(answer) for answer in answers_ref]\n",
    "    answers_pred = [clean_text(answer) for answer in answers_pred]\n",
    "\n",
    "    dfAnswers[\"Cleaned Answer\"] = answers_ref\n",
    "    dfAnswers[\"Cleaned Predicted Answer\"] = answers_pred\n",
    "\n",
    "    em = EmbeddingModelWrapper()\n",
    "    similarities = em.get_similarities(\n",
    "        em.get_embeddings( answers_pred ),\n",
    "        em.get_embeddings( answers_ref ),\n",
    "    )\n",
    "\n",
    "    dfAnswers[\"Similarity\"] = similarities\n",
    "\n",
    "    os.makedirs(BENCHMARKS_DIR, exist_ok=True)\n",
    "    dfAnswers.to_pickle(os.path.join(BENCHMARKS_DIR, f\"{name}.pkl\"))\n",
    "    return dfAnswers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retro-active Diversion - Llama Index's Chat Engine\n",
    "\n",
    "* After completing this notebook, including the benchmark comparison towards the end, I realized that Llama Index's chat engine is a good example of how to use a vector database to power a QA chatbot with minimal code.\n",
    "* So I decided to quickly benchmark it to see if I should include it in the final version of the project.\n",
    "* The default chat engine uses a retriever, just like my agent, then uses an LLM to answer questions using that retriever as a tool. \n",
    "* I'll use the same approach to benchmark this alternative agent:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43661bb4be16494388350e1a0cea1082",
       "version_major": 2,
       "version_minor": 0
      },
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       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chat_engine = data.index.as_chat_engine()\n",
    "answers_ref, answers_pred = [], []\n",
    "\n",
    "for title, context, question, answer, synthesized_question in tqdm(dfSample.values):\n",
    "    prompt = synthesized_question\n",
    "    final_answer = chat_engine.chat(prompt)\n",
    "    answers_ref.append(answer)\n",
    "    answers_pred.append(final_answer)\n",
    "\n",
    "answers_ref = [str(answer) for answer in answers_ref]\n",
    "answers_pred = [str(answer) for answer in answers_pred]\n",
    "\n",
    "dfAnswersCE = dfSample.copy()\n",
    "dfAnswersCE[\"Predicted Answer\"] = answers_pred\n",
    "\n",
    "dfAnswersCE = dfSample.copy()\n",
    "dfAnswersCE[\"Predicted Answer\"] = answers_pred\n",
    "\n",
    "# Remove stop words and punctuation from answers\n",
    "answers_ref = [clean_text(answer) for answer in answers_ref]\n",
    "answers_pred = [clean_text(answer) for answer in answers_pred]\n",
    "\n",
    "dfAnswersCE[\"Cleaned Answer\"] = answers_ref\n",
    "dfAnswersCE[\"Cleaned Predicted Answer\"] = answers_pred\n",
    "\n",
    "em = EmbeddingModelWrapper()\n",
    "similarities = em.get_similarities(\n",
    "    em.get_embeddings( answers_pred ),\n",
    "    em.get_embeddings( answers_ref ),\n",
    ")\n",
    "\n",
    "dfAnswersCE[\"Similarity\"] = similarities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Llama Index Chat Engine Mean Similarity: 0.45"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\n",
    "    Markdown(\n",
    "        f\"#### Llama Index Chat Engine Mean Similarity: {round(dfAnswersCE['Similarity'].mean(), 2)}\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "\n",
    "* The Llama Index Chat Engine has roughly the same mean semantic similarity as the `baseline` agent.\n",
    "* It doesn't seem like including it would add much value, so I'll stick with the agent variations I built using Transformers Agents 2.0.\n",
    "* Getting back to the original benchmarks:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up and run the benchmarks\n",
    "\n",
    "* We will run the agent with three different prompts:\n",
    "  * Baseline: The default transformers agent prompt modified only to use the squad_retriever tool.\n",
    "  * Succinct: The default prompt modified to encourage the agent to be more concise.\n",
    "  * Focused: The default prompt modified to encourage the agent to focus mostly on SQuAD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d96aeac5e244b28a1ec92d3e1ccc115",
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     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "98ef07cf016444b59dfc6d9c498d2eed",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d2ad4058ee14f858a155338c66c1d91",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d38df0a45c2a4514b058f7ce35112757",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the agents to be benchmarked\n",
    "benchmarks = [\n",
    "    {\"agent\": get_agent(system_prompt=DEFAULT_SQUAD_REACT_CODE_SYSTEM_PROMPT), \"name\": \"baseline\"}, # Baseline agent with default settings\n",
    "    {\"agent\": get_agent(system_prompt=SUCCINCT_SQUAD_REACT_CODE_SYSTEM_PROMPT), \"name\": \"succinct\"}, # Succinct agent\n",
    "    {\"agent\": get_agent(system_prompt=FOCUSED_SQUAD_REACT_CODE_SYSTEM_PROMPT), \"name\": \"focused\"}, # Focused agent\n",
    "]\n",
    "\n",
    "# Determine if all benchmark files already exist\n",
    "benchmark_names = [benchmark[\"name\"] for benchmark in benchmarks]\n",
    "all_benchmark_files_exist = all(os.path.exists(os.path.join(BENCHMARKS_DIR, f\"{name}.pkl\")) for name in benchmark_names)\n",
    "\n",
    "#if benchmark files do not exist, run the benchmarks\n",
    "if not all_benchmark_files_exist:\n",
    "    for benchmark in tqdm(benchmarks):\n",
    "        benchmark['data'] = benchmark_agent(benchmark[\"agent\"], dfSample, benchmark[\"name\"])\n",
    "else:\n",
    "    # if benchmark files exist, load them\n",
    "    for benchmark in tqdm(benchmarks):\n",
    "        benchmark['data'] = pd.read_pickle(os.path.join(BENCHMARKS_DIR, f\"{benchmark['name']}.pkl\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Review the benchmarks for each agent\n",
    "\n",
    "* We will review the benchmarks for each agent and plot the mean similarity and the distribution of semantic similarity scores across quartiles.\n",
    "* We will also plot the number of answers with a semantic similarity score >= a given threshold for each benchmark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add mean similarity to each benchmark\n",
    "for benchmark in benchmarks:\n",
    "    benchmark[\"mean_similarity\"] = benchmark[\"data\"][\"Similarity\"].mean()\n",
    "\n",
    "# Sort benchmarks by mean similarity\n",
    "benchmarks.sort(key=lambda x: x[\"mean_similarity\"], reverse=False)\n",
    "\n",
    "\n",
    "# Plot the mean similarity for each benchmark\n",
    "mean_similarities = [benchmark[\"mean_similarity\"] for benchmark in benchmarks]\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(9, 4.5))\n",
    "axs[0].bar(benchmark_names, mean_similarities)\n",
    "for i, v in enumerate(mean_similarities):\n",
    "    axs[0].text(i, v + 0.01, str(round(v, 2)), ha=\"center\", va=\"bottom\")\n",
    "axs[0].set_xlabel(\"Benchmark\")\n",
    "axs[0].set_ylabel(\"Mean Similarity\")\n",
    "axs[0].set_title(\"Mean Similarity\")\n",
    "\n",
    "# Plot the distribution of semantic similarity scores across quartiles\n",
    "\n",
    "# -- Create a dataframe with the quartile data for all benchmarks combined\n",
    "quartiles = [0.25, 0.5, 0.75]\n",
    "quartile_data = np.array([])\n",
    "quartile_names = np.array([])\n",
    "for benchmark in benchmarks:\n",
    "    df = benchmark[\"data\"]\n",
    "    semscores = np.array(df[\"Similarity\"].values)\n",
    "    quartile_data = np.append(quartile_data, np.digitize(semscores, quartiles))\n",
    "    quartile_names = np.append(quartile_names, [benchmark[\"name\"]] * len(semscores))\n",
    "\n",
    "df = pd.DataFrame({\"name\": quartile_names, \"quartile\": quartile_data})\n",
    "\n",
    "# -- Plot the distribution of semantic similarity scores across quartiles\n",
    "hue_order = list(df[\"quartile\"].unique()) # Best performers on top\n",
    "hue_order.sort(reverse=True)\n",
    "ax = sns.histplot(\n",
    "    df,\n",
    "    x=\"name\",\n",
    "    hue=\"quartile\",\n",
    "    multiple=\"stack\",\n",
    "    hue_order=hue_order,\n",
    "    palette=[\"#a2d9a4\", \"#47a0b3\", \"#fca55d\", \"#e2514a\"],\n",
    ")\n",
    "ax.set_xlabel(\"Benchmark\")\n",
    "ax.set_ylabel(\"Quartile\")\n",
    "ax.set_title(\"Distribution of Semantic Similarity Scores\")\n",
    "ax.legend(\n",
    "    title=\"Quartile\", labels=[\"Poor\", \"Needs Improvement\", \"Acceptable\", \"Excellent\"]\n",
    ")\n",
    "\n",
    "# -- Add the counts to the bars for easy reference\n",
    "for container in ax.containers:\n",
    "    labels = [\n",
    "        f\"{round(v.get_height())}\" if v.get_height() > 0 else \"\" for v in container\n",
    "    ]\n",
    "    ax.bar_label(container, labels=labels, label_type=\"center\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "\n",
    "* The `succinct` agent represents a significant improvement over the baseline, with a mean similarity of `0.83` and a distribution of semantic similarity scores across quartiles that is much closer to the top performer.\n",
    "* However, the `focused` agent out-performs the `succinct` agent, with a mean similarity of `0.85`, with a handful of better answers.\n",
    "* The performance is close enough that it may be worth while to look at the number of answers that would be considered correct across a range of possible thresholds. \n",
    "\n",
    "#### Number of answers with a semantic similarity score >= a given threshold\n",
    "\n",
    "* Note: that I show every possible threshold starting at 0.01, but in practice it's unlikely that a threshold of 0.01 would be used as a threshold for acceptable answers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the number of answers with a semantic similarity score >= a given threshold for each benchmark\n",
    "for benchmark in benchmarks:\n",
    "    thresholds = np.arange(0.01, 0.99, 0.01)\n",
    "    num_rows_above_threshold = []\n",
    "    for threshold in thresholds:\n",
    "        num_rows_above_threshold.append(len(benchmark['data'][benchmark['data']['Similarity'] >= threshold]))\n",
    "    benchmark['num_rows_above_threshold'] = num_rows_above_threshold\n",
    "\n",
    "for benchmark in benchmarks:\n",
    "    plt.plot(thresholds, benchmark['num_rows_above_threshold'], label=benchmark['name'])\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('Number of answers')\n",
    "plt.title('Number of answers with semantic similarity score >= threshold')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "\n",
    "* The `focused` agent outperforms the `succinct` agent by a small margin across most thresholds, until about 0.7, where it clearly outperforms the `succinct` agent.\n",
    "* Given this, I will deliver the `focused` agent as the best performing agent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exactly correct answers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show how many answers have Similarity == 1.0 exactly\n",
    "# Plot the number of answers with Similarity == 1.0 for each benchmark on a horizontal bar chart with seaborn\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "for benchmark in benchmarks:\n",
    "    ax.barh(benchmark['name'], benchmark['data'][benchmark['data']['Similarity'] == 1.0].shape[0])\n",
    "ax.set_xlabel('Number of answers')\n",
    "ax.set_ylabel('Benchmark')\n",
    "ax.set_title('Number of answers with Similarity == 1.0')\n",
    "for container in ax.containers:\n",
    "    ax.bar_label(container, label_type='edge')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "\n",
    "* It's impressive that the `focused` and `succinct` agents both got nearly 50% of the answers exactly correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Worse scoring answers for baseline"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "#T_63b9f_row0_col0, #T_63b9f_row0_col1, #T_63b9f_row0_col2, #T_63b9f_row0_col3, #T_63b9f_row1_col0, #T_63b9f_row1_col1, #T_63b9f_row1_col2, #T_63b9f_row1_col3, #T_63b9f_row2_col0, #T_63b9f_row2_col1, #T_63b9f_row2_col2, #T_63b9f_row2_col3, #T_63b9f_row3_col0, #T_63b9f_row3_col1, #T_63b9f_row3_col2, #T_63b9f_row3_col3, #T_63b9f_row4_col0, #T_63b9f_row4_col1, #T_63b9f_row4_col2, #T_63b9f_row4_col3, #T_63b9f_row5_col0, #T_63b9f_row5_col1, #T_63b9f_row5_col2, #T_63b9f_row5_col3, #T_63b9f_row6_col0, #T_63b9f_row6_col1, #T_63b9f_row6_col2, #T_63b9f_row6_col3, #T_63b9f_row7_col0, #T_63b9f_row7_col1, #T_63b9f_row7_col2, #T_63b9f_row7_col3, #T_63b9f_row8_col0, #T_63b9f_row8_col1, #T_63b9f_row8_col2, #T_63b9f_row8_col3, #T_63b9f_row9_col0, #T_63b9f_row9_col1, #T_63b9f_row9_col2, #T_63b9f_row9_col3 {\n",
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       "</style>\n",
       "<table id=\"T_63b9f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_63b9f_level0_col0\" class=\"col_heading level0 col0\" >Synthesized Question</th>\n",
       "      <th id=\"T_63b9f_level0_col1\" class=\"col_heading level0 col1\" >Answer</th>\n",
       "      <th id=\"T_63b9f_level0_col2\" class=\"col_heading level0 col2\" >Predicted Answer</th>\n",
       "      <th id=\"T_63b9f_level0_col3\" class=\"col_heading level0 col3\" >Similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_63b9f_level0_row0\" class=\"row_heading level0 row0\" >31138</th>\n",
       "      <td id=\"T_63b9f_row0_col0\" class=\"data row0 col0\" >How many species of fungi, both non-lichen-forming and lichen-forming, have been recorded in Antarctica?</td>\n",
       "      <td id=\"T_63b9f_row0_col1\" class=\"data row0 col1\" >1150</td>\n",
       "      <td id=\"T_63b9f_row0_col2\" class=\"data row0 col2\" >About 1150 species of fungi have been recorded in Antarctica, including approximately 750 non-lichen-forming and 400 lichen-forming species.</td>\n",
       "      <td id=\"T_63b9f_row0_col3\" class=\"data row0 col3\" >-0.037171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_63b9f_level0_row1\" class=\"row_heading level0 row1\" >56941</th>\n",
       "      <td id=\"T_63b9f_row1_col0\" class=\"data row1 col0\" >What fraction of the South Florida population lives in the city of Miami, given that it is home to less than one-thirteenth of the population of South Florida?</td>\n",
       "      <td id=\"T_63b9f_row1_col1\" class=\"data row1 col1\" >one-thirteenth</td>\n",
       "      <td id=\"T_63b9f_row1_col2\" class=\"data row1 col2\" >Approximately 7.69% of the South Florida population lives in the city of Miami.</td>\n",
       "      <td id=\"T_63b9f_row1_col3\" class=\"data row1 col3\" >0.011993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_63b9f_level0_row2\" class=\"row_heading level0 row2\" >31553</th>\n",
       "      <td id=\"T_63b9f_row2_col0\" class=\"data row2 col0\" >In how many scenarios projected by the ABS will Sydney remain higher than Melbourne in population beyond 2056?</td>\n",
       "      <td id=\"T_63b9f_row2_col1\" class=\"data row2 col1\" >two</td>\n",
       "      <td id=\"T_63b9f_row2_col2\" class=\"data row2 col2\" >In two scenarios projected by the ABS, Sydney will remain higher than Melbourne in population beyond 2056.</td>\n",
       "      <td id=\"T_63b9f_row2_col3\" class=\"data row2 col3\" >0.075540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_63b9f_level0_row3\" class=\"row_heading level0 row3\" >59122</th>\n",
       "      <td id=\"T_63b9f_row3_col0\" class=\"data row3 col0\" >How many companies were involved in the development of USB in 1994?</td>\n",
       "      <td id=\"T_63b9f_row3_col1\" class=\"data row3 col1\" >seven</td>\n",
       "      <td id=\"T_63b9f_row3_col2\" class=\"data row3 col2\" >Seven companies were involved in the development of USB in 1994: Compaq, DEC, IBM, Intel, Microsoft, NEC, and Nortel.</td>\n",
       "      <td id=\"T_63b9f_row3_col3\" class=\"data row3 col3\" >0.107220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_63b9f_level0_row4\" class=\"row_heading level0 row4\" >41377</th>\n",
       "      <td id=\"T_63b9f_row4_col0\" class=\"data row4 col0\" >How many Freistaaten (Free States) are there in Germany according to the current political structure?</td>\n",
       "      <td id=\"T_63b9f_row4_col1\" class=\"data row4 col1\" >three</td>\n",
       "      <td id=\"T_63b9f_row4_col2\" class=\"data row4 col2\" >There are 16 Freistaaten in Germany according to the current political structure.</td>\n",
       "      <td id=\"T_63b9f_row4_col3\" class=\"data row4 col3\" >0.131161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_63b9f_level0_row5\" class=\"row_heading level0 row5\" >20149</th>\n",
       "      <td id=\"T_63b9f_row5_col0\" class=\"data row5 col0\" >How large is the Marshall Islands shark sanctuary in square miles, specifically referring to the nearly 2,000,000 square kilometers designated by the government?</td>\n",
       "      <td id=\"T_63b9f_row5_col1\" class=\"data row5 col1\" >772,000</td>\n",
       "      <td id=\"T_63b9f_row5_col2\" class=\"data row5 col2\" >The Marshall Islands shark sanctuary is approximately 772,204 square miles in size.</td>\n",
       "      <td id=\"T_63b9f_row5_col3\" class=\"data row5 col3\" >0.135708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_63b9f_level0_row6\" class=\"row_heading level0 row6\" >51916</th>\n",
       "      <td id=\"T_63b9f_row6_col0\" class=\"data row6 col0\" >What kind of nutritional value do processed foods have in comparison to their fresh variants, considering the impact of processing techniques on nutrient content as described in the context of nutrition?</td>\n",
       "      <td id=\"T_63b9f_row6_col1\" class=\"data row6 col1\" >reduced</td>\n",
       "      <td id=\"T_63b9f_row6_col2\" class=\"data row6 col2\" >Processed foods generally have a reduced nutritional value compared to fresh foods, which are nutritionally superior. Processing techniques can lead to the loss of essential nutrients and may introduce harmful substances.</td>\n",
       "      <td id=\"T_63b9f_row6_col3\" class=\"data row6 col3\" >0.140512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_63b9f_level0_row7\" class=\"row_heading level0 row7\" >83184</th>\n",
       "      <td id=\"T_63b9f_row7_col0\" class=\"data row7 col0\" >In March 2014, what was the number of people sentenced to death during the single hearing by the Minya Criminal Court in Egypt?</td>\n",
       "      <td id=\"T_63b9f_row7_col1\" class=\"data row7 col1\" >529</td>\n",
       "      <td id=\"T_63b9f_row7_col2\" class=\"data row7 col2\" >I could not find the number of people sentenced to death during the single hearing by the Minya Criminal Court in Egypt in March 2014.</td>\n",
       "      <td id=\"T_63b9f_row7_col3\" class=\"data row7 col3\" >0.140897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_63b9f_level0_row8\" class=\"row_heading level0 row8\" >16329</th>\n",
       "      <td id=\"T_63b9f_row8_col0\" class=\"data row8 col0\" >On what date did ESPN announce the purchase of a minority stake in the Arena Football League (AFL)?</td>\n",
       "      <td id=\"T_63b9f_row8_col1\" class=\"data row8 col1\" >December 19, 2006</td>\n",
       "      <td id=\"T_63b9f_row8_col2\" class=\"data row8 col2\" >ESPN announced the purchase of a minority stake in the Arena Football League (AFL) on December 19, 2006.</td>\n",
       "      <td id=\"T_63b9f_row8_col3\" class=\"data row8 col3\" >0.144917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_63b9f_level0_row9\" class=\"row_heading level0 row9\" >6468</th>\n",
       "      <td id=\"T_63b9f_row9_col0\" class=\"data row9 col0\" >What does the Sanskrit term \"Karma,\" which translates to \"action\" or \"work,\" refer to in the context of Buddhism?</td>\n",
       "      <td id=\"T_63b9f_row9_col1\" class=\"data row9 col1\" >action, work</td>\n",
       "      <td id=\"T_63b9f_row9_col2\" class=\"data row9 col2\" >In Buddhism, the Sanskrit term 'Karma' refers to the force that drives saṃsāra, the cycle of suffering and rebirth. It encompasses actions of body, speech, or mind that arise from mental intent and produce consequences, with good actions leading to positive outcomes and bad actions leading to negative consequences.</td>\n",
       "      <td id=\"T_63b9f_row9_col3\" class=\"data row9 col3\" >0.168453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x43c757170>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Worse scoring answers for succinct"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_512f6_level0_col0\" class=\"col_heading level0 col0\" >Synthesized Question</th>\n",
       "      <th id=\"T_512f6_level0_col1\" class=\"col_heading level0 col1\" >Answer</th>\n",
       "      <th id=\"T_512f6_level0_col2\" class=\"col_heading level0 col2\" >Predicted Answer</th>\n",
       "      <th id=\"T_512f6_level0_col3\" class=\"col_heading level0 col3\" >Similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_512f6_level0_row0\" class=\"row_heading level0 row0\" >31138</th>\n",
       "      <td id=\"T_512f6_row0_col0\" class=\"data row0 col0\" >How many species of fungi, both non-lichen-forming and lichen-forming, have been recorded in Antarctica?</td>\n",
       "      <td id=\"T_512f6_row0_col1\" class=\"data row0 col1\" >1150</td>\n",
       "      <td id=\"T_512f6_row0_col2\" class=\"data row0 col2\" >1150 species of fungi have been recorded in Antarctica.</td>\n",
       "      <td id=\"T_512f6_row0_col3\" class=\"data row0 col3\" >-0.002039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_512f6_level0_row1\" class=\"row_heading level0 row1\" >79931</th>\n",
       "      <td id=\"T_512f6_row1_col0\" class=\"data row1 col0\" >Where was very expensive wallpaper imported from during the Georgian architectural period?</td>\n",
       "      <td id=\"T_512f6_row1_col1\" class=\"data row1 col1\" >China</td>\n",
       "      <td id=\"T_512f6_row1_col2\" class=\"data row1 col2\" >The information about the origin of very expensive wallpaper during the Georgian architectural period is not available in the dataset.</td>\n",
       "      <td id=\"T_512f6_row1_col3\" class=\"data row1 col3\" >0.165669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_512f6_level0_row2\" class=\"row_heading level0 row2\" >60191</th>\n",
       "      <td id=\"T_512f6_row2_col0\" class=\"data row2 col0\" >According to Hayek, limited government power through the Rule of Law does not stultify individual efforts by ad hoc action. What does this mean for people's ability to make investments and future plans?</td>\n",
       "      <td id=\"T_512f6_row2_col1\" class=\"data row2 col1\" >frustrate his efforts</td>\n",
       "      <td id=\"T_512f6_row2_col2\" class=\"data row2 col2\" >According to Hayek, limited government power through the Rule of Law enables individuals to confidently make investments and future plans without fear of arbitrary government interference.</td>\n",
       "      <td id=\"T_512f6_row2_col3\" class=\"data row2 col3\" >0.172209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_512f6_level0_row3\" class=\"row_heading level0 row3\" >51916</th>\n",
       "      <td id=\"T_512f6_row3_col0\" class=\"data row3 col0\" >What kind of nutritional value do processed foods have in comparison to their fresh variants, considering the impact of processing techniques on nutrient content as described in the context of nutrition?</td>\n",
       "      <td id=\"T_512f6_row3_col1\" class=\"data row3 col1\" >reduced</td>\n",
       "      <td id=\"T_512f6_row3_col2\" class=\"data row3 col2\" >Processed foods have a reduced nutritional value compared to fresh foods.</td>\n",
       "      <td id=\"T_512f6_row3_col3\" class=\"data row3 col3\" >0.174138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_512f6_level0_row4\" class=\"row_heading level0 row4\" >6468</th>\n",
       "      <td id=\"T_512f6_row4_col0\" class=\"data row4 col0\" >What does the Sanskrit term \"Karma,\" which translates to \"action\" or \"work,\" refer to in the context of Buddhism?</td>\n",
       "      <td id=\"T_512f6_row4_col1\" class=\"data row4 col1\" >action, work</td>\n",
       "      <td id=\"T_512f6_row4_col2\" class=\"data row4 col2\" >In Buddhism, Karma refers to the force that drives saṃsāra, encompassing actions of body, speech, or mind that stem from mental intent and produce consequences.</td>\n",
       "      <td id=\"T_512f6_row4_col3\" class=\"data row4 col3\" >0.266618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_512f6_level0_row5\" class=\"row_heading level0 row5\" >49374</th>\n",
       "      <td id=\"T_512f6_row5_col0\" class=\"data row5 col0\" >Who is elected as mayor every even-numbered year in Ann Arbor, Michigan?</td>\n",
       "      <td id=\"T_512f6_row5_col1\" class=\"data row5 col1\" >mayor</td>\n",
       "      <td id=\"T_512f6_row5_col2\" class=\"data row5 col2\" >Christopher Taylor</td>\n",
       "      <td id=\"T_512f6_row5_col3\" class=\"data row5 col3\" >0.288215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_512f6_level0_row6\" class=\"row_heading level0 row6\" >69135</th>\n",
       "      <td id=\"T_512f6_row6_col0\" class=\"data row6 col0\" >How many engineering colleges are there in Rajasthan, as mentioned in the context provided?</td>\n",
       "      <td id=\"T_512f6_row6_col1\" class=\"data row6 col1\" >41 engineering colleges</td>\n",
       "      <td id=\"T_512f6_row6_col2\" class=\"data row6 col2\" >41</td>\n",
       "      <td id=\"T_512f6_row6_col3\" class=\"data row6 col3\" >0.322794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_512f6_level0_row7\" class=\"row_heading level0 row7\" >5111</th>\n",
       "      <td id=\"T_512f6_row7_col0\" class=\"data row7 col0\" >When did Tajiks begin to be conscripted into the Soviet Army, particularly during the lead-up to and including World War II?</td>\n",
       "      <td id=\"T_512f6_row7_col1\" class=\"data row7 col1\" >1939</td>\n",
       "      <td id=\"T_512f6_row7_col2\" class=\"data row7 col2\" >Tajiks began to be conscripted into the Soviet Army in 1939.</td>\n",
       "      <td id=\"T_512f6_row7_col3\" class=\"data row7 col3\" >0.329918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_512f6_level0_row8\" class=\"row_heading level0 row8\" >13080</th>\n",
       "      <td id=\"T_512f6_row8_col0\" class=\"data row8 col0\" >What impact does temperature have on the variability of hunter-gatherer tool kits?</td>\n",
       "      <td id=\"T_512f6_row8_col1\" class=\"data row8 col1\" >increased variability of tools</td>\n",
       "      <td id=\"T_512f6_row8_col2\" class=\"data row8 col2\" >Temperature increases the variability of hunter-gatherer tool kits.</td>\n",
       "      <td id=\"T_512f6_row8_col3\" class=\"data row8 col3\" >0.341771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_512f6_level0_row9\" class=\"row_heading level0 row9\" >69990</th>\n",
       "      <td id=\"T_512f6_row9_col0\" class=\"data row9 col0\" >When was ZE Records, the no wave-affiliated label mentioned in the context of post-punk, founded?</td>\n",
       "      <td id=\"T_512f6_row9_col1\" class=\"data row9 col1\" >1978</td>\n",
       "      <td id=\"T_512f6_row9_col2\" class=\"data row9 col2\" >ZE Records was founded in 1978. It is known for its association with the no wave movement and its influence on the post-punk music scene.</td>\n",
       "      <td id=\"T_512f6_row9_col3\" class=\"data row9 col3\" >0.343213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x43d860e30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Worse scoring answers for focused"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_310bb_level0_col0\" class=\"col_heading level0 col0\" >Synthesized Question</th>\n",
       "      <th id=\"T_310bb_level0_col1\" class=\"col_heading level0 col1\" >Answer</th>\n",
       "      <th id=\"T_310bb_level0_col2\" class=\"col_heading level0 col2\" >Predicted Answer</th>\n",
       "      <th id=\"T_310bb_level0_col3\" class=\"col_heading level0 col3\" >Similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_310bb_level0_row0\" class=\"row_heading level0 row0\" >31138</th>\n",
       "      <td id=\"T_310bb_row0_col0\" class=\"data row0 col0\" >How many species of fungi, both non-lichen-forming and lichen-forming, have been recorded in Antarctica?</td>\n",
       "      <td id=\"T_310bb_row0_col1\" class=\"data row0 col1\" >1150</td>\n",
       "      <td id=\"T_310bb_row0_col2\" class=\"data row0 col2\" >1150 species of fungi have been recorded in Antarctica, including 750 non-lichen-forming and 400 lichen-forming species.</td>\n",
       "      <td id=\"T_310bb_row0_col3\" class=\"data row0 col3\" >-0.034279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_310bb_level0_row1\" class=\"row_heading level0 row1\" >51916</th>\n",
       "      <td id=\"T_310bb_row1_col0\" class=\"data row1 col0\" >What kind of nutritional value do processed foods have in comparison to their fresh variants, considering the impact of processing techniques on nutrient content as described in the context of nutrition?</td>\n",
       "      <td id=\"T_310bb_row1_col1\" class=\"data row1 col1\" >reduced</td>\n",
       "      <td id=\"T_310bb_row1_col2\" class=\"data row1 col2\" >Processed foods have reduced nutritional value compared to fresh foods.</td>\n",
       "      <td id=\"T_310bb_row1_col3\" class=\"data row1 col3\" >0.174138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_310bb_level0_row2\" class=\"row_heading level0 row2\" >56941</th>\n",
       "      <td id=\"T_310bb_row2_col0\" class=\"data row2 col0\" >What fraction of the South Florida population lives in the city of Miami, given that it is home to less than one-thirteenth of the population of South Florida?</td>\n",
       "      <td id=\"T_310bb_row2_col1\" class=\"data row2 col1\" >one-thirteenth</td>\n",
       "      <td id=\"T_310bb_row2_col2\" class=\"data row2 col2\" >less than 1/13</td>\n",
       "      <td id=\"T_310bb_row2_col3\" class=\"data row2 col3\" >0.192653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_310bb_level0_row3\" class=\"row_heading level0 row3\" >6468</th>\n",
       "      <td id=\"T_310bb_row3_col0\" class=\"data row3 col0\" >What does the Sanskrit term \"Karma,\" which translates to \"action\" or \"work,\" refer to in the context of Buddhism?</td>\n",
       "      <td id=\"T_310bb_row3_col1\" class=\"data row3 col1\" >action, work</td>\n",
       "      <td id=\"T_310bb_row3_col2\" class=\"data row3 col2\" >In Buddhism, 'Karma' refers to the actions of body, speech, or mind that spring from mental intent and drive the cycle of suffering and rebirth (saṃsāra).</td>\n",
       "      <td id=\"T_310bb_row3_col3\" class=\"data row3 col3\" >0.245145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_310bb_level0_row4\" class=\"row_heading level0 row4\" >60191</th>\n",
       "      <td id=\"T_310bb_row4_col0\" class=\"data row4 col0\" >According to Hayek, limited government power through the Rule of Law does not stultify individual efforts by ad hoc action. What does this mean for people's ability to make investments and future plans?</td>\n",
       "      <td id=\"T_310bb_row4_col1\" class=\"data row4 col1\" >frustrate his efforts</td>\n",
       "      <td id=\"T_310bb_row4_col2\" class=\"data row4 col2\" >Hayek believes that limited government power through the Rule of Law allows individuals to make wise investments and future plans with confidence, as it prevents the government from frustrating their efforts.</td>\n",
       "      <td id=\"T_310bb_row4_col3\" class=\"data row4 col3\" >0.251189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_310bb_level0_row5\" class=\"row_heading level0 row5\" >79931</th>\n",
       "      <td id=\"T_310bb_row5_col0\" class=\"data row5 col0\" >Where was very expensive wallpaper imported from during the Georgian architectural period?</td>\n",
       "      <td id=\"T_310bb_row5_col1\" class=\"data row5 col1\" >China</td>\n",
       "      <td id=\"T_310bb_row5_col2\" class=\"data row5 col2\" >Very expensive wallpaper during the Georgian architectural period was primarily imported from France and China.</td>\n",
       "      <td id=\"T_310bb_row5_col3\" class=\"data row5 col3\" >0.277804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_310bb_level0_row6\" class=\"row_heading level0 row6\" >49374</th>\n",
       "      <td id=\"T_310bb_row6_col0\" class=\"data row6 col0\" >Who is elected as mayor every even-numbered year in Ann Arbor, Michigan?</td>\n",
       "      <td id=\"T_310bb_row6_col1\" class=\"data row6 col1\" >mayor</td>\n",
       "      <td id=\"T_310bb_row6_col2\" class=\"data row6 col2\" >Christopher Taylor</td>\n",
       "      <td id=\"T_310bb_row6_col3\" class=\"data row6 col3\" >0.288215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_310bb_level0_row7\" class=\"row_heading level0 row7\" >5111</th>\n",
       "      <td id=\"T_310bb_row7_col0\" class=\"data row7 col0\" >When did Tajiks begin to be conscripted into the Soviet Army, particularly during the lead-up to and including World War II?</td>\n",
       "      <td id=\"T_310bb_row7_col1\" class=\"data row7 col1\" >1939</td>\n",
       "      <td id=\"T_310bb_row7_col2\" class=\"data row7 col2\" >Tajiks began to be conscripted into the Soviet Army in 1939.</td>\n",
       "      <td id=\"T_310bb_row7_col3\" class=\"data row7 col3\" >0.329918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_310bb_level0_row8\" class=\"row_heading level0 row8\" >13080</th>\n",
       "      <td id=\"T_310bb_row8_col0\" class=\"data row8 col0\" >What impact does temperature have on the variability of hunter-gatherer tool kits?</td>\n",
       "      <td id=\"T_310bb_row8_col1\" class=\"data row8 col1\" >increased variability of tools</td>\n",
       "      <td id=\"T_310bb_row8_col2\" class=\"data row8 col2\" >Temperature increases the variability of hunter-gatherer tool kits.</td>\n",
       "      <td id=\"T_310bb_row8_col3\" class=\"data row8 col3\" >0.341771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_310bb_level0_row9\" class=\"row_heading level0 row9\" >45263</th>\n",
       "      <td id=\"T_310bb_row9_col0\" class=\"data row9 col0\" >How many copies has Queen's Greatest Hits II sold worldwide?</td>\n",
       "      <td id=\"T_310bb_row9_col1\" class=\"data row9 col1\" >16 million</td>\n",
       "      <td id=\"T_310bb_row9_col2\" class=\"data row9 col2\" >The specific sales figures for Queen's Greatest Hits II are not available, but Queen has sold over 150 million records worldwide in total.</td>\n",
       "      <td id=\"T_310bb_row9_col3\" class=\"data row9 col3\" >0.345838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x43c7551c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show the worst 10 answers for each benchmark\n",
    "for benchmark in benchmarks:\n",
    "    display(Markdown(f\"#### Worse scoring answers for {benchmark['name']}\"))\n",
    "    display_text_df(\n",
    "        benchmark[\"data\"][\n",
    "            [\"Synthesized Question\", \"Answer\", \"Predicted Answer\", \"Similarity\"]\n",
    "        ]\n",
    "        .sort_values(by=\"Similarity\", ascending=True)\n",
    "        .head(10)\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "\n",
    "* All of the agents are unfairly penalized for elaborating on answers when the reference answer is brief. \n",
    "  * For example, in the worst-ranking answer when all 3 agents are correct (id=31138), but ranked the worst in semantic similarity due to the extra context they provided.\n",
    "* Of the `baseline` agent's worst answers by semantic similarity, 3 are unquestionably incorrect, while the rest look correct from a human evaluator's perspective.\n",
    "* Of the `succinct` agent's worst answers by semantic similarity, 2 are unquestionably incorrect, while the rest look correct from a human evaluator's perspective.\n",
    "* Of the `focused` agent's worst answers by semantic similarity, 2 are unquestionably incorrect, while the rest look correct from a human evaluator's perspective."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### Best scoring answers for baseline"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<table id=\"T_3c767\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_3c767_level0_col0\" class=\"col_heading level0 col0\" >Synthesized Question</th>\n",
       "      <th id=\"T_3c767_level0_col1\" class=\"col_heading level0 col1\" >Answer</th>\n",
       "      <th id=\"T_3c767_level0_col2\" class=\"col_heading level0 col2\" >Predicted Answer</th>\n",
       "      <th id=\"T_3c767_level0_col3\" class=\"col_heading level0 col3\" >Similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_3c767_level0_row0\" class=\"row_heading level0 row0\" >56283</th>\n",
       "      <td id=\"T_3c767_row0_col0\" class=\"data row0 col0\" >While Darwin wasn't sure about blending inherited traits, what was his opinion on the usefulness of inherited variation in relation to his concept of selection as discussed in \"On the Origin of Species\"?</td>\n",
       "      <td id=\"T_3c767_row0_col1\" class=\"data row0 col1\" >Darwin's concept of selection working on a population with a range of small variations was workable</td>\n",
       "      <td id=\"T_3c767_row0_col2\" class=\"data row0 col2\" >Darwin believed that inherited variation was useful because his concept of selection could work on a population with a range of small variations.</td>\n",
       "      <td id=\"T_3c767_row0_col3\" class=\"data row0 col3\" >0.830038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3c767_level0_row1\" class=\"row_heading level0 row1\" >15471</th>\n",
       "      <td id=\"T_3c767_row1_col0\" class=\"data row1 col0\" >What is the name of the main public service broadcaster in Somalia, as mentioned in the context of Communications in Somalia?</td>\n",
       "      <td id=\"T_3c767_row1_col1\" class=\"data row1 col1\" >Somali National Television</td>\n",
       "      <td id=\"T_3c767_row1_col2\" class=\"data row1 col2\" >The main public service broadcaster in Somalia is Somali National Television (SNTV).</td>\n",
       "      <td id=\"T_3c767_row1_col3\" class=\"data row1 col3\" >0.816376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3c767_level0_row2\" class=\"row_heading level0 row2\" >39287</th>\n",
       "      <td id=\"T_3c767_row2_col0\" class=\"data row2 col0\" >What specific educational reforms implemented by Napoleon contributed to the establishment of a modernized education system in France and influenced education across Europe?</td>\n",
       "      <td id=\"T_3c767_row2_col1\" class=\"data row2 col1\" >Napoleon's educational reforms</td>\n",
       "      <td id=\"T_3c767_row2_col2\" class=\"data row2 col2\" >Napoleon implemented several educational reforms that contributed to a modernized education system in France and influenced Europe. These reforms included the establishment of state secondary schools (lycées) to provide standardized education, the emphasis on teaching sciences and languages, and the development of a secular public education system. He aimed to synthesize the best elements from the Ancien Régime, the Enlightenment, and the Revolution, and his system featured scholarships and strict discipline, resulting in France's educational system outperforming its European counterparts.</td>\n",
       "      <td id=\"T_3c767_row2_col3\" class=\"data row2 col3\" >0.810252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3c767_level0_row3\" class=\"row_heading level0 row3\" >36802</th>\n",
       "      <td id=\"T_3c767_row3_col0\" class=\"data row3 col0\" >Who is responsible for approving drugs for use in the United Kingdom, according to the context of the pharmaceutical industry?</td>\n",
       "      <td id=\"T_3c767_row3_col1\" class=\"data row3 col1\" >Medicines and Healthcare Products Regulatory Agency</td>\n",
       "      <td id=\"T_3c767_row3_col2\" class=\"data row3 col2\" >The Medicines and Healthcare Products Regulatory Agency (MHRA) is responsible for approving drugs for use in the United Kingdom.</td>\n",
       "      <td id=\"T_3c767_row3_col3\" class=\"data row3 col3\" >0.810035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3c767_level0_row4\" class=\"row_heading level0 row4\" >56292</th>\n",
       "      <td id=\"T_3c767_row4_col0\" class=\"data row4 col0\" >How does Darwin theorize that instincts have evolved in slave-making ants in Chapter VII of \"On the Origin of Species\"?</td>\n",
       "      <td id=\"T_3c767_row4_col1\" class=\"data row4 col1\" >He thought it reasonable that species with an extreme dependency on slave workers had evolved in incremental steps.</td>\n",
       "      <td id=\"T_3c767_row4_col2\" class=\"data row4 col2\" >Darwin theorized that instincts in slave-making ants evolved in incremental steps, particularly in species that exhibit extreme dependency on slave workers.</td>\n",
       "      <td id=\"T_3c767_row4_col3\" class=\"data row4 col3\" >0.809778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3c767_level0_row5\" class=\"row_heading level0 row5\" >74315</th>\n",
       "      <td id=\"T_3c767_row5_col0\" class=\"data row5 col0\" >What is the name of the famous valley located in Oregon, specifically mentioned in the context of the Geography of the United States?</td>\n",
       "      <td id=\"T_3c767_row5_col1\" class=\"data row5 col1\" >Willamette Valley</td>\n",
       "      <td id=\"T_3c767_row5_col2\" class=\"data row5 col2\" >The famous valley located in Oregon is the Willamette Valley.</td>\n",
       "      <td id=\"T_3c767_row5_col3\" class=\"data row5 col3\" >0.800726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3c767_level0_row6\" class=\"row_heading level0 row6\" >32186</th>\n",
       "      <td id=\"T_3c767_row6_col0\" class=\"data row6 col0\" >What did the Observer Corps in Britain observe and report on regarding hostile aircraft flying over Britain?</td>\n",
       "      <td id=\"T_3c767_row6_col1\" class=\"data row6 col1\" >hostile aircraft flying over Britain</td>\n",
       "      <td id=\"T_3c767_row6_col2\" class=\"data row6 col2\" >The Observer Corps in Britain observed and reported on hostile aircraft flying over Britain.</td>\n",
       "      <td id=\"T_3c767_row6_col3\" class=\"data row6 col3\" >0.787479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3c767_level0_row7\" class=\"row_heading level0 row7\" >69135</th>\n",
       "      <td id=\"T_3c767_row7_col0\" class=\"data row7 col0\" >How many engineering colleges are there in Rajasthan, as mentioned in the context provided?</td>\n",
       "      <td id=\"T_3c767_row7_col1\" class=\"data row7 col1\" >41 engineering colleges</td>\n",
       "      <td id=\"T_3c767_row7_col2\" class=\"data row7 col2\" >There are 41 engineering colleges in Rajasthan.</td>\n",
       "      <td id=\"T_3c767_row7_col3\" class=\"data row7 col3\" >0.774651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3c767_level0_row8\" class=\"row_heading level0 row8\" >70472</th>\n",
       "      <td id=\"T_3c767_row8_col0\" class=\"data row8 col0\" >What factors contributed to Prussia's decision not to renew its alliance with Britain after the Seven Years' War?</td>\n",
       "      <td id=\"T_3c767_row8_col1\" class=\"data row8 col1\" >Prussians were angered by what they considered a British betrayal in 1762</td>\n",
       "      <td id=\"T_3c767_row8_col2\" class=\"data row8 col2\" >Prussia decided not to renew its alliance with Britain after the Seven Years' War primarily due to their anger over what they considered a British betrayal in 1762.</td>\n",
       "      <td id=\"T_3c767_row8_col3\" class=\"data row8 col3\" >0.772004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3c767_level0_row9\" class=\"row_heading level0 row9\" >81021</th>\n",
       "      <td id=\"T_3c767_row9_col0\" class=\"data row9 col0\" >Where were the advanced materials and techniques, such as buttresses and arches, used in Sumerian temples and palaces on display?</td>\n",
       "      <td id=\"T_3c767_row9_col1\" class=\"data row9 col1\" >Sumerian temples and palaces</td>\n",
       "      <td id=\"T_3c767_row9_col2\" class=\"data row9 col2\" >The advanced materials and techniques, such as buttresses and arches, used in Sumerian temples and palaces are on display in Sumerian temples and palaces.</td>\n",
       "      <td id=\"T_3c767_row9_col3\" class=\"data row9 col3\" >0.766647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x43d4c7170>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "#### Best scoring answers for succinct"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_23984_level0_col0\" class=\"col_heading level0 col0\" >Synthesized Question</th>\n",
       "      <th id=\"T_23984_level0_col1\" class=\"col_heading level0 col1\" >Answer</th>\n",
       "      <th id=\"T_23984_level0_col2\" class=\"col_heading level0 col2\" >Predicted Answer</th>\n",
       "      <th id=\"T_23984_level0_col3\" class=\"col_heading level0 col3\" >Similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_23984_level0_row0\" class=\"row_heading level0 row0\" >55901</th>\n",
       "      <td id=\"T_23984_row0_col0\" class=\"data row0 col0\" >In 1849, what railroad was created by the North Carolina legislature to extend west from the Wilmington and Raleigh Railroad?</td>\n",
       "      <td id=\"T_23984_row0_col1\" class=\"data row0 col1\" >North Carolina Railroad</td>\n",
       "      <td id=\"T_23984_row0_col2\" class=\"data row0 col2\" >North Carolina Railroad</td>\n",
       "      <td id=\"T_23984_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23984_level0_row1\" class=\"row_heading level0 row1\" >34182</th>\n",
       "      <td id=\"T_23984_row1_col0\" class=\"data row1 col0\" >What ordering scheme did readers prefer during the Age of Enlightenment?</td>\n",
       "      <td id=\"T_23984_row1_col1\" class=\"data row1 col1\" >alphabetical</td>\n",
       "      <td id=\"T_23984_row1_col2\" class=\"data row1 col2\" >alphabetical</td>\n",
       "      <td id=\"T_23984_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23984_level0_row2\" class=\"row_heading level0 row2\" >9983</th>\n",
       "      <td id=\"T_23984_row2_col0\" class=\"data row2 col0\" >What year was the Banská Akadémia, the world's first institution of technology, founded in Banská Štiavnica, Slovakia?</td>\n",
       "      <td id=\"T_23984_row2_col1\" class=\"data row2 col1\" >1735</td>\n",
       "      <td id=\"T_23984_row2_col2\" class=\"data row2 col2\" >1735</td>\n",
       "      <td id=\"T_23984_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23984_level0_row3\" class=\"row_heading level0 row3\" >17172</th>\n",
       "      <td id=\"T_23984_row3_col0\" class=\"data row3 col0\" >What year did Bern join the Swiss Confederacy, according to the historical context provided?</td>\n",
       "      <td id=\"T_23984_row3_col1\" class=\"data row3 col1\" >1353</td>\n",
       "      <td id=\"T_23984_row3_col2\" class=\"data row3 col2\" >1353</td>\n",
       "      <td id=\"T_23984_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23984_level0_row4\" class=\"row_heading level0 row4\" >87251</th>\n",
       "      <td id=\"T_23984_row4_col0\" class=\"data row4 col0\" >Where can safari hunters go that are considered uninviting to typical ecotourists, particularly in the context of hunting in Tanzania?</td>\n",
       "      <td id=\"T_23984_row4_col1\" class=\"data row4 col1\" >remote areas</td>\n",
       "      <td id=\"T_23984_row4_col2\" class=\"data row4 col2\" >remote areas</td>\n",
       "      <td id=\"T_23984_row4_col3\" class=\"data row4 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23984_level0_row5\" class=\"row_heading level0 row5\" >65484</th>\n",
       "      <td id=\"T_23984_row5_col0\" class=\"data row5 col0\" >What field did the majority of the 144,600 employees at La Défense work in as of 2010?</td>\n",
       "      <td id=\"T_23984_row5_col1\" class=\"data row5 col1\" >finance and insurance</td>\n",
       "      <td id=\"T_23984_row5_col2\" class=\"data row5 col2\" >finance and insurance</td>\n",
       "      <td id=\"T_23984_row5_col3\" class=\"data row5 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23984_level0_row6\" class=\"row_heading level0 row6\" >74315</th>\n",
       "      <td id=\"T_23984_row6_col0\" class=\"data row6 col0\" >What is the name of the famous valley located in Oregon, specifically mentioned in the context of the Geography of the United States?</td>\n",
       "      <td id=\"T_23984_row6_col1\" class=\"data row6 col1\" >Willamette Valley</td>\n",
       "      <td id=\"T_23984_row6_col2\" class=\"data row6 col2\" >Willamette Valley</td>\n",
       "      <td id=\"T_23984_row6_col3\" class=\"data row6 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23984_level0_row7\" class=\"row_heading level0 row7\" >43267</th>\n",
       "      <td id=\"T_23984_row7_col0\" class=\"data row7 col0\" >What is another speed rating that can also be reported by the camera in addition to the noise-based and saturation-based speeds?</td>\n",
       "      <td id=\"T_23984_row7_col1\" class=\"data row7 col1\" >SOS-based speed</td>\n",
       "      <td id=\"T_23984_row7_col2\" class=\"data row7 col2\" >SOS-based speed</td>\n",
       "      <td id=\"T_23984_row7_col3\" class=\"data row7 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23984_level0_row8\" class=\"row_heading level0 row8\" >16329</th>\n",
       "      <td id=\"T_23984_row8_col0\" class=\"data row8 col0\" >On what date did ESPN announce the purchase of a minority stake in the Arena Football League (AFL)?</td>\n",
       "      <td id=\"T_23984_row8_col1\" class=\"data row8 col1\" >December 19, 2006</td>\n",
       "      <td id=\"T_23984_row8_col2\" class=\"data row8 col2\" >December 19, 2006</td>\n",
       "      <td id=\"T_23984_row8_col3\" class=\"data row8 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_23984_level0_row9\" class=\"row_heading level0 row9\" >58193</th>\n",
       "      <td id=\"T_23984_row9_col0\" class=\"data row9 col0\" >At around what age, specifically twenty-four or twenty-five, was Dominic ordained as a Priest in the context of the Dominican Order?</td>\n",
       "      <td id=\"T_23984_row9_col1\" class=\"data row9 col1\" >twenty-five</td>\n",
       "      <td id=\"T_23984_row9_col2\" class=\"data row9 col2\" >twenty-five</td>\n",
       "      <td id=\"T_23984_row9_col3\" class=\"data row9 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "<pandas.io.formats.style.Styler at 0x43d5ec950>"
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     "data": {
      "text/markdown": [
       "#### Best scoring answers for focused"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
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       "<table id=\"T_be7b7\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_be7b7_level0_col0\" class=\"col_heading level0 col0\" >Synthesized Question</th>\n",
       "      <th id=\"T_be7b7_level0_col1\" class=\"col_heading level0 col1\" >Answer</th>\n",
       "      <th id=\"T_be7b7_level0_col2\" class=\"col_heading level0 col2\" >Predicted Answer</th>\n",
       "      <th id=\"T_be7b7_level0_col3\" class=\"col_heading level0 col3\" >Similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_be7b7_level0_row0\" class=\"row_heading level0 row0\" >34182</th>\n",
       "      <td id=\"T_be7b7_row0_col0\" class=\"data row0 col0\" >What ordering scheme did readers prefer during the Age of Enlightenment?</td>\n",
       "      <td id=\"T_be7b7_row0_col1\" class=\"data row0 col1\" >alphabetical</td>\n",
       "      <td id=\"T_be7b7_row0_col2\" class=\"data row0 col2\" >alphabetical</td>\n",
       "      <td id=\"T_be7b7_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be7b7_level0_row1\" class=\"row_heading level0 row1\" >3951</th>\n",
       "      <td id=\"T_be7b7_row1_col0\" class=\"data row1 col0\" >In 1860, approximately how many Irish immigrants were living in New York City?</td>\n",
       "      <td id=\"T_be7b7_row1_col1\" class=\"data row1 col1\" >200,000</td>\n",
       "      <td id=\"T_be7b7_row1_col2\" class=\"data row1 col2\" >Over 200,000</td>\n",
       "      <td id=\"T_be7b7_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be7b7_level0_row2\" class=\"row_heading level0 row2\" >55901</th>\n",
       "      <td id=\"T_be7b7_row2_col0\" class=\"data row2 col0\" >In 1849, what railroad was created by the North Carolina legislature to extend west from the Wilmington and Raleigh Railroad?</td>\n",
       "      <td id=\"T_be7b7_row2_col1\" class=\"data row2 col1\" >North Carolina Railroad</td>\n",
       "      <td id=\"T_be7b7_row2_col2\" class=\"data row2 col2\" >North Carolina Railroad</td>\n",
       "      <td id=\"T_be7b7_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be7b7_level0_row3\" class=\"row_heading level0 row3\" >81694</th>\n",
       "      <td id=\"T_be7b7_row3_col0\" class=\"data row3 col0\" >When did the United States purchase Alaska from Russia?</td>\n",
       "      <td id=\"T_be7b7_row3_col1\" class=\"data row3 col1\" >1867</td>\n",
       "      <td id=\"T_be7b7_row3_col2\" class=\"data row3 col2\" >1867</td>\n",
       "      <td id=\"T_be7b7_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be7b7_level0_row4\" class=\"row_heading level0 row4\" >6266</th>\n",
       "      <td id=\"T_be7b7_row4_col0\" class=\"data row4 col0\" >Where was Donda West's funeral held on November 20, 2007?</td>\n",
       "      <td id=\"T_be7b7_row4_col1\" class=\"data row4 col1\" >Oklahoma City</td>\n",
       "      <td id=\"T_be7b7_row4_col2\" class=\"data row4 col2\" >Oklahoma City</td>\n",
       "      <td id=\"T_be7b7_row4_col3\" class=\"data row4 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be7b7_level0_row5\" class=\"row_heading level0 row5\" >2267</th>\n",
       "      <td id=\"T_be7b7_row5_col0\" class=\"data row5 col0\" >How many households were the offices of Qianhu in charge of during the Ming dynasty as described in the context of Sino-Tibetan relations?</td>\n",
       "      <td id=\"T_be7b7_row5_col1\" class=\"data row5 col1\" >1,000 households</td>\n",
       "      <td id=\"T_be7b7_row5_col2\" class=\"data row5 col2\" >1,000 households</td>\n",
       "      <td id=\"T_be7b7_row5_col3\" class=\"data row5 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be7b7_level0_row6\" class=\"row_heading level0 row6\" >72912</th>\n",
       "      <td id=\"T_be7b7_row6_col0\" class=\"data row6 col0\" >What year did the government start distributing Morrison shelters during The Blitz?</td>\n",
       "      <td id=\"T_be7b7_row6_col1\" class=\"data row6 col1\" >1941</td>\n",
       "      <td id=\"T_be7b7_row6_col2\" class=\"data row6 col2\" >1941</td>\n",
       "      <td id=\"T_be7b7_row6_col3\" class=\"data row6 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be7b7_level0_row7\" class=\"row_heading level0 row7\" >40855</th>\n",
       "      <td id=\"T_be7b7_row7_col0\" class=\"data row7 col0\" >Who was the chief engineer at the United States Electric Lighting Company, as mentioned in the context of the development of the incandescent light bulb?</td>\n",
       "      <td id=\"T_be7b7_row7_col1\" class=\"data row7 col1\" >Hiram S. Maxim</td>\n",
       "      <td id=\"T_be7b7_row7_col2\" class=\"data row7 col2\" >Hiram S. Maxim</td>\n",
       "      <td id=\"T_be7b7_row7_col3\" class=\"data row7 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be7b7_level0_row8\" class=\"row_heading level0 row8\" >8786</th>\n",
       "      <td id=\"T_be7b7_row8_col0\" class=\"data row8 col0\" >What was Alfred North Whitehead's final area of study before developing his comprehensive metaphysical system?</td>\n",
       "      <td id=\"T_be7b7_row8_col1\" class=\"data row8 col1\" >metaphysics</td>\n",
       "      <td id=\"T_be7b7_row8_col2\" class=\"data row8 col2\" >metaphysics</td>\n",
       "      <td id=\"T_be7b7_row8_col3\" class=\"data row8 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be7b7_level0_row9\" class=\"row_heading level0 row9\" >58193</th>\n",
       "      <td id=\"T_be7b7_row9_col0\" class=\"data row9 col0\" >At around what age, specifically twenty-four or twenty-five, was Dominic ordained as a Priest in the context of the Dominican Order?</td>\n",
       "      <td id=\"T_be7b7_row9_col1\" class=\"data row9 col1\" >twenty-five</td>\n",
       "      <td id=\"T_be7b7_row9_col2\" class=\"data row9 col2\" >twenty-five</td>\n",
       "      <td id=\"T_be7b7_row9_col3\" class=\"data row9 col3\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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       "<pandas.io.formats.style.Styler at 0x43d863380>"
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     },
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    }
   ],
   "source": [
    "# Show the best 10 answers for each benchmark\n",
    "for benchmark in benchmarks:\n",
    "    display(Markdown(f\"#### Best scoring answers for {benchmark['name']}\"))\n",
    "    display_text_df(benchmark['data'][['Synthesized Question', 'Answer', 'Predicted Answer', 'Similarity']].sort_values(by='Similarity', ascending=False).head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "\n",
    "* Here we can see `focused` and `succeinct` agents are producing exactly correct answers, while even at its best, the `baseline` agent is producing factually relevant and correct answers, but being penalized for elaborating and providing more context. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Overall, the semantic similarity metric differentiates between good and bad answers, but is prone to penalizing agents for elaborating on answers when the reference answer is brief.  \n",
    "\n",
    "## Future Work\n",
    "\n",
    "* It may be interesting to have human-generated, contextualized acceptable answers to go with the concise answers, and then take the max sementic similarity score between the predicted answer and the acceptable answers, to avoid penalizing agents for providing relevant context.\n",
    "* It would also be interesting to look for and include the other acceptable answers found in the SQuAD dataset. "
   ]
  }
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