Answers.Body: <p>There's been a recent paper (July 2024) that covers a lot on the topic of prompting LLMs: <a href="https://arxiv.org/pdf/2406.06608" rel="nofollow noreferrer">&quot;The Prompt Report: A Systematic Survey of Prompting Techniques&quot;</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-09-27T14:35:15.883Z
Answers.LastActivityDate: 2024-09-27T14:35:15.883Z
Answers.UserDisplayName: talles
Answers.Body: <p>Currently, I am referring to the following links. They have a good number of papers and they are categorized too.</p>
  <p><a href="https://github.com/thunlp/PromptPapers" rel="nofollow noreferrer">https://github.com/thunlp/PromptPapers</a></p>
  <p><a href="https://github.com/zjunlp/PromptKG" rel="nofollow noreferrer">https://github.com/zjunlp/PromptKG</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-01-26T14:32:47.000Z
Answers.Text: While this link may answer the question, it is better to include the essential parts of the answer here and provide the link for reference.  Link-only answers can become invalid if the linked page changes. - [From Review](/review/late-answers/24165)
Answers.UserDisplayName: Rob
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-01-26T05:07:34.310Z
Answers.LastActivityDate: 2023-01-26T05:07:34.310Z
Answers.UserDisplayName: kelly
Answers.Body: <p>Prompt Engineer - If you're interested in studying prompt engineering, especially how to teach models like GPT-4 to reason, there are several key papers and resources you can start with. These papers focus on how prompt design can influence model behaviour and reasoning capabilities. Below are some recommended readings:</p>
  <ol>
  <li>&quot;Language Models are Few-Shot Learners&quot; (Brown et al., 2020)
  •   Link: Language Models are Few-Shot Learners, <a href="https://arxiv.org/abs/2005.14165" rel="nofollow noreferrer">https://arxiv.org/abs/2005.14165</a>
  •   Summary: This paper introduces GPT-3 and demonstrates how large language models can perform tasks with little or no task-specific training by simply using prompts. It explores in-context learning, few-shot, and zero-shot prompting.</li>
  </ol>
  <p>•   Relevance: A foundational paper for understanding how language models can reason and perform tasks based on different prompt formats.
  2. &quot;Chain-of-Thought Prompting Elicits Reasoning in Large Language Models&quot; (Wei et al., 2022)
  •   Link: Chain-of-Thought Prompting, <a href="https://arxiv.org/abs/2005.14165" rel="nofollow noreferrer">https://arxiv.org/abs/2201.11903</a>
  •   Summary: This paper introduces the &quot;chain-of-thought&quot; (CoT) technique, which involves prompting the model to provide intermediate steps when solving reasoning tasks. This helps the model break down complex problems and improve performance on reasoning-heavy tasks.
  •   Relevance: CoT prompting is essential for teaching models how to reason explicitly. This is a must-read for learning about reasoning techniques in prompt engineering.</p>
  <ol start="3">
  <li>&quot;Self-Consistency Improves Chain-of-Thought Reasoning in Language Models&quot; (Wang et al., 2022)
  •   Link: Self-Consistency in CoT Reasoning, <a href="https://arxiv.org/abs/2205.11916" rel="nofollow noreferrer">https://arxiv.org/abs/2203.11171</a>
  •   Summary: This paper builds on chain-of-thought prompting by introducing a &quot;self-consistency&quot; method. Instead of relying on a single response, the model generates multiple reasoning paths and selects the most consistent one.</li>
  </ol>
  <p>•   Relevance: This approach improves reasoning accuracy and offers an advanced technique for reasoning-focused prompt engineering.
  4. &quot;Large Language Models are Zero-Shot Reasoners&quot; (Kojima et al., 2022)
  •   Link: Zero-Shot Reasoners, <a href="https://arxiv.org/abs/2205.11916" rel="nofollow noreferrer">https://arxiv.org/abs/2205.11916</a>
  •   Summary: This paper shows that even without explicit training, large language models can be effective zero-shot reasoners when prompted with &quot;Let’s think step by step.&quot; This demonstrates the power of careful prompt phrasing in reasoning.
  •   Relevance: The paper highlights how simple modifications to prompts can lead to significantly better reasoning in zero-shot scenarios.</p>
  <ol start="5">
  <li><p>&quot;Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm&quot; (Reynolds &amp; McDonell, 2021)
  •   Link: Prompt Programming, <a href="https://arxiv.org/abs/2102.07350" rel="nofollow noreferrer">https://arxiv.org/abs/2102.07350</a>
  •   Summary: This paper discusses the concept of &quot;prompt programming,&quot; which involves designing prompts to program the behaviour of large language models in specific ways. It extends prompt engineering beyond few-shot learning.
  •   Relevance: A great resource for understanding how prompt design can be used to control and guide model behaviour beyond basic instruction-following.</p>
  </li>
  <li><p>&quot;Instruction Tuning with GPT-4&quot; (OpenAI, 2023)
  •   Relevance: While there may not be a dedicated paper yet, instruction tuning is a key aspect of making GPT-4 and similar models follow instructions and reason based on prompts. Following OpenAI's blog and documentation on instruction-following models is also helpful.
  •   Resources: You can check the OpenAI Blog for relevant updates.</p>
  </li>
  <li><p>&quot;On the Opportunities and Risks of Foundation Models&quot; (Bommasani et al., 2021)
  •   Link: Foundation Models, <a href="https://arxiv.org/abs/2108.07258" rel="nofollow noreferrer">https://arxiv.org/abs/2108.07258</a>
  •   Summary: This paper discusses the broader implications and capabilities of large foundation models like GPT. It covers opportunities in prompt engineering, transfer learning, and multi-modal reasoning.</p>
  </li>
  </ol>
  <p>•   Relevance: Provides a broader context for the power and risks of language models, including their behaviour with different prompts.
  Other Resources:
  •   Anthropic's &quot;Constitutional AI&quot;: This work explores how aligning language models with ethical guidelines via prompts can steer their behaviour. It has valuable insights on guiding models’ reasoning and behaviour through prompt engineering.</p>
  <p>•   Stanford's HELM (Holistic Evaluation of Language Models): While this is more focused on evaluation, it provides insights into how different prompts can lead to varied behaviour in models.</p>
  <p>Prompt Engineer: A portal full of lots of Prompt Engineering info and resources, <a href="https://promptengineer-1.weebly.com" rel="nofollow noreferrer">https://promptengineer-1.weebly.com</a></p>
  <p>Key Techniques in Prompt Engineering for Reasoning:</p>
  <ol>
  <li><p>Chain-of-Thought Prompts: Break problems into logical steps to improve reasoning.</p>
  </li>
  <li><p>Self-Consistency: Use multiple reasoning paths and select the most consistent one.</p>
  </li>
  <li><p>Instruction Tuning: Provide models with clear and explicit instructions to guide behaviour.</p>
  </li>
  <li><p>Zero-Shot Prompts: Simple prompt phrasing like &quot;Let’s think step by step&quot; can enhance reasoning.</p>
  </li>
  <li><p>Role-Playing Prompts: Assign the model a role (e.g., teacher, expert) to elicit specific behaviours.</p>
  </li>
  </ol>
  <p>These papers should provide a solid foundation in prompt engineering, especially for improving reasoning capabilities in large language models like GPT-4.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-09-27T06:00:18.027Z
Answers.LastActivityDate: 2024-09-27T06:00:18.027Z
Answers.UserDisplayName: Prompt Engineer
Body: <p>I am into AI in general and NLP in particular. Besides, I have a background in philosophy, and the new LLMs like GPT-3 seem to have exciting capabilities. I want to study prompt engineering (for example, teaching the model to reason, etc.)</p>
  <p>Do you know any specific papers to start? I am looking for studies on the techniques of prompt engineering.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-01-24T11:02:50.277Z
Comments.Text: I've reformulated the title because the previous title asked for "best paper" and that can lead to opinions. In fact, this post was voted to be closed as "opinion-based".
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-01-25T16:39:06.437Z
Comments.Text: This should help https://github.com/rynst/awesome-llm-engineering
Comments.UserDisplayName: Minh-Long Luu
Tags: <natural-language-processing><reference-request><papers><language-model><gpt-3>
Title: Papers on Prompt Engineering
UserDisplayName: abbassix
ThreadText: # Papers on Prompt Engineering
  
  (Asked by: username_0 on 2022-09-27)
  
  I am into AI in general and NLP in particular. Besides, I have a background in philosophy, and the new LLMs like GPT-3 seem to have exciting capabilities. I want to study prompt engineering (for example, teaching the model to reason, etc.)
  
  Do you know any specific papers to start? I am looking for studies on the techniques of prompt engineering.
  
  **Tags:** natural-language-processing, reference-request, papers, language-model, gpt-3
  > **Comments:**
  > **username_1**: I've reformulated the title because the previous title asked for "best paper" and that can lead to opinions. In fact, this post was voted to be closed as "opinion-based".  
  > **username_2**: This should help https://github.com/rynst/awesome-llm-engineering  
  
  ---
  
  ## 3 answers
  
  ### Answer by username_3 on 2024-09-27. Score: 1
  
  There's been a recent paper (July 2024) that covers a lot on the topic of prompting LLMs: ["The Prompt Report: A Systematic Survey of Prompting Techniques"](https://arxiv.org/pdf/2406.06608)
  
  
  ---
  
  ### Answer by username_4 on 2023-01-26. Score: 1
  
  Currently, I am referring to the following links. They have a good number of papers and they are categorized too.
  
  <https://github.com/thunlp/PromptPapers>
  
  <https://github.com/zjunlp/PromptKG>
  
  > **Comments:**
  > **username_5**: While this link may answer the question, it is better to include the essential parts of the answer here and provide the link for reference.  Link-only answers can become invalid if the linked page changes. - [From Review](/review/late-answers/24165)  
  
  ---
  
  ### Answer by username_6 on 2024-09-27. Score: 0
  
  Prompt Engineer - If you're interested in studying prompt engineering, especially how to teach models like GPT-4 to reason, there are several key papers and resources you can start with. These papers focus on how prompt design can influence model behaviour and reasoning capabilities. Below are some recommended readings:
  
  1. "Language Models are Few-Shot Learners" (Brown et al., 2020)
     • Link: Language Models are Few-Shot Learners, <https://arxiv.org/abs/2005.14165>
     • Summary: This paper introduces GPT-3 and demonstrates how large language models can perform tasks with little or no task-specific training by simply using prompts. It explores in-context learning, few-shot, and zero-shot prompting.
  
  • Relevance: A foundational paper for understanding how language models can reason and perform tasks based on different prompt formats.
  2. "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" (Wei et al., 2022)
  • Link: Chain-of-Thought Prompting, [https://arxiv.org/abs/2201.11903](https://arxiv.org/abs/2005.14165)
  • Summary: This paper introduces the "chain-of-thought" (CoT) technique, which involves prompting the model to provide intermediate steps when solving reasoning tasks. This helps the model break down complex problems and improve performance on reasoning-heavy tasks.
  • Relevance: CoT prompting is essential for teaching models how to reason explicitly. This is a must-read for learning about reasoning techniques in prompt engineering.
  
  3. "Self-Consistency Improves Chain-of-Thought Reasoning in Language Models" (Wang et al., 2022)
     • Link: Self-Consistency in CoT Reasoning, [https://arxiv.org/abs/2203.11171](https://arxiv.org/abs/2205.11916)
     • Summary: This paper builds on chain-of-thought prompting by introducing a "self-consistency" method. Instead of relying on a single response, the model generates multiple reasoning paths and selects the most consistent one.
  
  • Relevance: This approach improves reasoning accuracy and offers an advanced technique for reasoning-focused prompt engineering.
  4. "Large Language Models are Zero-Shot Reasoners" (Kojima et al., 2022)
  • Link: Zero-Shot Reasoners, <https://arxiv.org/abs/2205.11916>
  • Summary: This paper shows that even without explicit training, large language models can be effective zero-shot reasoners when prompted with "Let’s think step by step." This demonstrates the power of careful prompt phrasing in reasoning.
  • Relevance: The paper highlights how simple modifications to prompts can lead to significantly better reasoning in zero-shot scenarios.
  
  5. "Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm" (Reynolds & McDonell, 2021)
     • Link: Prompt Programming, <https://arxiv.org/abs/2102.07350>
     • Summary: This paper discusses the concept of "prompt programming," which involves designing prompts to program the behaviour of large language models in specific ways. It extends prompt engineering beyond few-shot learning.
     • Relevance: A great resource for understanding how prompt design can be used to control and guide model behaviour beyond basic instruction-following.
  6. "Instruction Tuning with GPT-4" (OpenAI, 2023)
     • Relevance: While there may not be a dedicated paper yet, instruction tuning is a key aspect of making GPT-4 and similar models follow instructions and reason based on prompts. Following OpenAI's blog and documentation on instruction-following models is also helpful.
     • Resources: You can check the OpenAI Blog for relevant updates.
  7. "On the Opportunities and Risks of Foundation Models" (Bommasani et al., 2021)
     • Link: Foundation Models, <https://arxiv.org/abs/2108.07258>
     • Summary: This paper discusses the broader implications and capabilities of large foundation models like GPT. It covers opportunities in prompt engineering, transfer learning, and multi-modal reasoning.
  
  • Relevance: Provides a broader context for the power and risks of language models, including their behaviour with different prompts.
  Other Resources:
  • Anthropic's "Constitutional AI": This work explores how aligning language models with ethical guidelines via prompts can steer their behaviour. It has valuable insights on guiding models’ reasoning and behaviour through prompt engineering.
  
  • Stanford's HELM (Holistic Evaluation of Language Models): While this is more focused on evaluation, it provides insights into how different prompts can lead to varied behaviour in models.
  
  Prompt Engineer: A portal full of lots of Prompt Engineering info and resources, <https://promptengineer-1.weebly.com>
  
  Key Techniques in Prompt Engineering for Reasoning:
  
  1. Chain-of-Thought Prompts: Break problems into logical steps to improve reasoning.
  2. Self-Consistency: Use multiple reasoning paths and select the most consistent one.
  3. Instruction Tuning: Provide models with clear and explicit instructions to guide behaviour.
  4. Zero-Shot Prompts: Simple prompt phrasing like "Let’s think step by step" can enhance reasoning.
  5. Role-Playing Prompts: Assign the model a role (e.g., teacher, expert) to elicit specific behaviours.
  
  These papers should provide a solid foundation in prompt engineering, especially for improving reasoning capabilities in large language models like GPT-4.
  
  
  ---
  
  