title: Long-form factuality in large language models
summary: Large language models (LLMs) often generate content that contains factual
  errors when responding to fact-seeking prompts on open-ended topics. To
  benchmark a model's long-form factuality in open domains, we first use GPT-4 to
  generate LongFact, a prompt set comprising thousands of questions spanning 38
  topics. We then propose that LLM agents can be used as automated evaluators for
  long-form factuality through a method which we call Search-Augmented Factuality
  Evaluator (SAFE). SAFE utilizes an LLM to break down a long-form response into
  a set of individual facts and to evaluate the accuracy of each fact using a
  multi-step reasoning process comprising sending search queries to Google Search
  and determining whether a fact is supported by the search results. Furthermore,
  we propose extending F1 score as an aggregated metric for long-form factuality.
  To do so, we balance the percentage of supported facts in a response
  (precision) with the percentage of provided facts relative to a hyperparameter
  representing a user's preferred response length (recall).
    Empirically, we demonstrate that LLM agents can achieve superhuman rating
  performance - on a set of ~16k individual facts, SAFE agrees with crowdsourced
  human annotators 72% of the time, and on a random subset of 100 disagreement
  cases, SAFE wins 76% of the time. At the same time, SAFE is more than 20 times
  cheaper than human annotators. We also benchmark thirteen language models on
  LongFact across four model families (Gemini, GPT, Claude, and PaLM-2), finding
  that larger language models generally achieve better long-form factuality.
  LongFact, SAFE, and all experimental code are available at
  https://github.com/google-deepmind/long-form-factuality.
paper_authors._id: 6604cf6dd8ae250868da1544
paper_authors.name: Jerry Wei
paper_authors._id: 6604cf6dd8ae250868da1545
paper_authors.name: Chengrun Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-06T15:19:47.245Z
paper_authors._id: 64fbab29e0dc35986bac8e03
paper_authors.avatarUrl: /avatars/7c11abfac56a06c1122750332c40d189.svg
paper_authors.fullname: Chengrun Yang
paper_authors.type: user
paper_authors.user: chengrun
paper_authors._id: 6604cf6dd8ae250868da1546
paper_authors.name: Xinying Song
paper_authors._id: 6604cf6dd8ae250868da1547
paper_authors.name: Yifeng Lu
paper_authors._id: 6604cf6dd8ae250868da1548
paper_authors.name: Nathan Hu
paper_authors._id: 6604cf6dd8ae250868da1549
paper_authors.name: Dustin Tran
paper_authors._id: 6604cf6dd8ae250868da154a
paper_authors.name: Daiyi Peng
paper_authors._id: 6604cf6dd8ae250868da154b
paper_authors.name: Ruibo Liu
paper_authors._id: 6604cf6dd8ae250868da154c
paper_authors.name: Da Huang
paper_authors._id: 6604cf6dd8ae250868da154d
paper_authors.name: Cosmo Du
paper_authors._id: 6604cf6dd8ae250868da154e
paper_authors.name: Quoc V. Le
paper_title: Long-form factuality in large language models
paper_summary: Large language models (LLMs) often generate content that contains factual
  errors when responding to fact-seeking prompts on open-ended topics. To
  benchmark a model's long-form factuality in open domains, we first use GPT-4 to
  generate LongFact, a prompt set comprising thousands of questions spanning 38
  topics. We then propose that LLM agents can be used as automated evaluators for
  long-form factuality through a method which we call Search-Augmented Factuality
  Evaluator (SAFE). SAFE utilizes an LLM to break down a long-form response into
  a set of individual facts and to evaluate the accuracy of each fact using a
  multi-step reasoning process comprising sending search queries to Google Search
  and determining whether a fact is supported by the search results. Furthermore,
  we propose extending F1 score as an aggregated metric for long-form factuality.
  To do so, we balance the percentage of supported facts in a response
  (precision) with the percentage of provided facts relative to a hyperparameter
  representing a user's preferred response length (recall).
    Empirically, we demonstrate that LLM agents can achieve superhuman rating
  performance - on a set of ~16k individual facts, SAFE agrees with crowdsourced
  human annotators 72% of the time, and on a random subset of 100 disagreement
  cases, SAFE wins 76% of the time. At the same time, SAFE is more than 20 times
  cheaper than human annotators. We also benchmark thirteen language models on
  LongFact across four model families (Gemini, GPT, Claude, and PaLM-2), finding
  that larger language models generally achieve better long-form factuality.
  LongFact, SAFE, and all experimental code are available at
  https://github.com/google-deepmind/long-form-factuality.
paper_ai_summary: SAFE, a novel method using LLMs and Google Search, evaluates long-form factual accuracy in LLM responses more cheaply and accurately than human annotators.
paper_ai_keywords: LLMs Large language models LongFact Search-Augmented Factuality Evaluator SAFE multi-step reasoning F1 score precision recall crowdsourced human annotators Gemini GPT Claude PaLM-2