Split off related resources (#730)

README.md

@@ -9,6 +9,7 @@ Most code examples are written in Python, though the concepts can be applied in
 [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=468576060&machine=basicLinux32gb&location=EastUs)
 
 ## Recently added/updated 🆕 ✨
+
 - [How to fine-tune chat models](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_finetune_chat_models.ipynb) [Aug 22, 2023]
 - [How to evaluate abstractive summarization](examples/evaluation/How_to_eval_abstractive_summarization.ipynb) [Aug 16, 2023]
 - [Whisper prompting guide](examples/Whisper_prompting_guide.ipynb) [June 27, 2023]
@@ -69,58 +70,6 @@ Beyond the code examples here, you can learn about the [OpenAI API] from the fol
 - See example prompts in the [OpenAI Examples]
 - Stay updated with the [OpenAI Blog]
 
-## Related resources from around the web
-
-People are writing great tools and papers for improving outputs from GPT. Here are some cool ones we've seen:
-
-### Prompting libraries & tools
-
-- [Guidance](https://github.com/microsoft/guidance): A handy looking Python library from Microsoft that uses Handlebars templating to interleave generation, prompting, and logical control.
-- [LangChain](https://github.com/hwchase17/langchain): A popular Python/JavaScript library for chaining sequences of language model prompts.
-- [FLAML (A Fast Library for Automated Machine Learning & Tuning)](https://microsoft.github.io/FLAML/docs/Getting-Started/): A Python library for automating selection of models, hyperparameters, and other tunable choices.
-- [Chainlit](https://docs.chainlit.io/overview): A Python library for making chatbot interfaces.
-- [Guardrails.ai](https://shreyar.github.io/guardrails/): A Python library for validating outputs and retrying failures. Still in alpha, so expect sharp edges and bugs.
-- [Semantic Kernel](https://github.com/microsoft/semantic-kernel): A Python/C#/Java library from Microsoft that supports prompt templating, function chaining, vectorized memory, and intelligent planning.
-- [Prompttools](https://github.com/hegelai/prompttools): Open-source Python tools for testing and evaluating models, vector DBs, and prompts.
-- [Outlines](https://github.com/normal-computing/outlines): A Python library that provides a domain-specific language to simplify prompting and constrain generation.
-- [Promptify](https://github.com/promptslab/Promptify): A small Python library for using language models to perform NLP tasks.
-- [Scale Spellbook](https://scale.com/spellbook): A paid product for building, comparing, and shipping language model apps.
-- [PromptPerfect](https://promptperfect.jina.ai/prompts): A paid product for testing and improving prompts.
-- [Weights & Biases](https://wandb.ai/site/solutions/llmops): A paid product for tracking model training and prompt engineering experiments.
-- [OpenAI Evals](https://github.com/openai/evals): An open-source library for evaluating task performance of language models and prompts.
-- [LlamaIndex](https://github.com/jerryjliu/llama_index): A Python library for augmenting LLM apps with data.
-- [Arthur Shield](https://www.arthur.ai/get-started): A paid product for detecting toxicity, hallucination, prompt injection, etc.
-- [LMQL](https://lmql.ai): A programming language for LLM interaction with support for typed prompting, control flow, constraints, and tools.
-
-### Prompting guides
-
-- [Brex's Prompt Engineering Guide](https://github.com/brexhq/prompt-engineering): Brex's introduction to language models and prompt engineering.
-- [promptingguide.ai](https://www.promptingguide.ai/): A prompt engineering guide that demonstrates many techniques.
-- [OpenAI Cookbook: Techniques to improve reliability](https://github.com/openai/openai-cookbook/blob/main/techniques_to_improve_reliability.md): A slightly dated (Sep 2022) review of techniques for prompting language models.
-- [Lil'Log Prompt Engineering](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/): An OpenAI researcher's review of the prompt engineering literature (as of March 2023).
-- [learnprompting.org](https://learnprompting.org/): An introductory course to prompt engineering.
-
-### Video courses
-
-- [Andrew Ng's DeepLearning.AI](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/): A short course on prompt engineering for developers.
-- [Andrej Karpathy's Let's build GPT](https://www.youtube.com/watch?v=kCc8FmEb1nY): A detailed dive into the machine learning underlying GPT.
-- [Prompt Engineering by DAIR.AI](https://www.youtube.com/watch?v=dOxUroR57xs): A one-hour video on various prompt engineering techniques.
-
-### Papers on advanced prompting to improve reasoning
-
-- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2022)](https://arxiv.org/abs/2201.11903): Using few-shot prompts to ask models to think step by step improves their reasoning. PaLM's score on math word problems (GSM8K) rises from 18% to 57%.
-- [Self-Consistency Improves Chain of Thought Reasoning in Language Models (2022)](https://arxiv.org/abs/2203.11171): Taking votes from multiple outputs improves accuracy even more. Voting across 40 outputs raises PaLM's score on math word problems further, from 57% to 74%, and `code-davinci-002`'s from 60% to 78%.
-- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models (2023)](https://arxiv.org/abs/2305.10601): Searching over trees of step by step reasoning helps even more than voting over chains of thought. It lifts `GPT-4`'s scores on creative writing and crosswords.
-- [Language Models are Zero-Shot Reasoners (2022)](https://arxiv.org/abs/2205.11916): Telling instruction-following models to think step by step improves their reasoning. It lifts `text-davinci-002`'s score on math word problems (GSM8K) from 13% to 41%.
-- [Large Language Models Are Human-Level Prompt Engineers (2023)](https://arxiv.org/abs/2211.01910): Automated searching over possible prompts found a prompt that lifts scores on math word problems (GSM8K) to 43%, 2 percentage points above the human-written prompt in Language Models are Zero-Shot Reasoners.
-- [Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling (2023)](https://arxiv.org/abs/2305.09993): Automated searching over possible chain-of-thought prompts improved ChatGPT's scores on a few benchmarks by 0–20 percentage points.
-- [Faithful Reasoning Using Large Language Models (2022)](https://arxiv.org/abs/2208.14271): Reasoning can be improved by a system that combines: chains of thought generated by alternative selection and inference prompts, a halter model that chooses when to halt selection-inference loops, a value function to search over multiple reasoning paths, and sentence labels that help avoid hallucination.
-- [STaR: Bootstrapping Reasoning With Reasoning (2022)](https://arxiv.org/abs/2203.14465): Chain of thought reasoning can be baked into models via fine-tuning. For tasks with an answer key, example chains of thoughts can be generated by language models.
-- [ReAct: Synergizing Reasoning and Acting in Language Models (2023)](https://arxiv.org/abs/2210.03629): For tasks with tools or an environment, chain of thought works better you prescriptively alternate between **Re**asoning steps (thinking about what to do) and **Act**ing (getting information from a tool or environment).
-- [Reflexion: an autonomous agent with dynamic memory and self-reflection (2023)](https://arxiv.org/abs/2303.11366): Retrying tasks with memory of prior failures improves subsequent performance.
-- [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP (2023)](https://arxiv.org/abs/2212.14024): Models augmented with knowledge via a "retrieve-then-read" can be improved with multi-hop chains of searches.
-- [Improving Factuality and Reasoning in Language Models through Multiagent Debate (2023)](https://arxiv.org/abs/2305.14325): Generating debates between a few ChatGPT agents over a few rounds improves scores on various benchmarks. Math word problem scores rise from 77% to 85%.
-
 ## Contributing
 
 If there are examples or guides you'd like to see, feel free to suggest them on the [issues page]. We are also happy to accept high quality pull requests, as long as they fit the scope of the repo.

related_resources.md

@@ -0,0 +1,51 @@
+# Related resources from around the web
+
+People are writing great tools and papers for improving outputs from GPT. Here are some cool ones we've seen:
+
+## Prompting libraries & tools
+
+- [Guidance](https://github.com/microsoft/guidance): A handy looking Python library from Microsoft that uses Handlebars templating to interleave generation, prompting, and logical control.
+- [LangChain](https://github.com/hwchase17/langchain): A popular Python/JavaScript library for chaining sequences of language model prompts.
+- [FLAML (A Fast Library for Automated Machine Learning & Tuning)](https://microsoft.github.io/FLAML/docs/Getting-Started/): A Python library for automating selection of models, hyperparameters, and other tunable choices.
+- [Chainlit](https://docs.chainlit.io/overview): A Python library for making chatbot interfaces.
+- [Guardrails.ai](https://shreyar.github.io/guardrails/): A Python library for validating outputs and retrying failures. Still in alpha, so expect sharp edges and bugs.
+- [Semantic Kernel](https://github.com/microsoft/semantic-kernel): A Python/C#/Java library from Microsoft that supports prompt templating, function chaining, vectorized memory, and intelligent planning.
+- [Prompttools](https://github.com/hegelai/prompttools): Open-source Python tools for testing and evaluating models, vector DBs, and prompts.
+- [Outlines](https://github.com/normal-computing/outlines): A Python library that provides a domain-specific language to simplify prompting and constrain generation.
+- [Promptify](https://github.com/promptslab/Promptify): A small Python library for using language models to perform NLP tasks.
+- [Scale Spellbook](https://scale.com/spellbook): A paid product for building, comparing, and shipping language model apps.
+- [PromptPerfect](https://promptperfect.jina.ai/prompts): A paid product for testing and improving prompts.
+- [Weights & Biases](https://wandb.ai/site/solutions/llmops): A paid product for tracking model training and prompt engineering experiments.
+- [OpenAI Evals](https://github.com/openai/evals): An open-source library for evaluating task performance of language models and prompts.
+- [LlamaIndex](https://github.com/jerryjliu/llama_index): A Python library for augmenting LLM apps with data.
+- [Arthur Shield](https://www.arthur.ai/get-started): A paid product for detecting toxicity, hallucination, prompt injection, etc.
+- [LMQL](https://lmql.ai): A programming language for LLM interaction with support for typed prompting, control flow, constraints, and tools.
+
+## Prompting guides
+
+- [Brex's Prompt Engineering Guide](https://github.com/brexhq/prompt-engineering): Brex's introduction to language models and prompt engineering.
+- [promptingguide.ai](https://www.promptingguide.ai/): A prompt engineering guide that demonstrates many techniques.
+- [OpenAI Cookbook: Techniques to improve reliability](https://github.com/openai/openai-cookbook/blob/main/techniques_to_improve_reliability.md): A slightly dated (Sep 2022) review of techniques for prompting language models.
+- [Lil'Log Prompt Engineering](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/): An OpenAI researcher's review of the prompt engineering literature (as of March 2023).
+- [learnprompting.org](https://learnprompting.org/): An introductory course to prompt engineering.
+
+## Video courses
+
+- [Andrew Ng's DeepLearning.AI](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/): A short course on prompt engineering for developers.
+- [Andrej Karpathy's Let's build GPT](https://www.youtube.com/watch?v=kCc8FmEb1nY): A detailed dive into the machine learning underlying GPT.
+- [Prompt Engineering by DAIR.AI](https://www.youtube.com/watch?v=dOxUroR57xs): A one-hour video on various prompt engineering techniques.
+
+## Papers on advanced prompting to improve reasoning
+
+- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2022)](https://arxiv.org/abs/2201.11903): Using few-shot prompts to ask models to think step by step improves their reasoning. PaLM's score on math word problems (GSM8K) rises from 18% to 57%.
+- [Self-Consistency Improves Chain of Thought Reasoning in Language Models (2022)](https://arxiv.org/abs/2203.11171): Taking votes from multiple outputs improves accuracy even more. Voting across 40 outputs raises PaLM's score on math word problems further, from 57% to 74%, and `code-davinci-002`'s from 60% to 78%.
+- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models (2023)](https://arxiv.org/abs/2305.10601): Searching over trees of step by step reasoning helps even more than voting over chains of thought. It lifts `GPT-4`'s scores on creative writing and crosswords.
+- [Language Models are Zero-Shot Reasoners (2022)](https://arxiv.org/abs/2205.11916): Telling instruction-following models to think step by step improves their reasoning. It lifts `text-davinci-002`'s score on math word problems (GSM8K) from 13% to 41%.
+- [Large Language Models Are Human-Level Prompt Engineers (2023)](https://arxiv.org/abs/2211.01910): Automated searching over possible prompts found a prompt that lifts scores on math word problems (GSM8K) to 43%, 2 percentage points above the human-written prompt in Language Models are Zero-Shot Reasoners.
+- [Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling (2023)](https://arxiv.org/abs/2305.09993): Automated searching over possible chain-of-thought prompts improved ChatGPT's scores on a few benchmarks by 0–20 percentage points.
+- [Faithful Reasoning Using Large Language Models (2022)](https://arxiv.org/abs/2208.14271): Reasoning can be improved by a system that combines: chains of thought generated by alternative selection and inference prompts, a halter model that chooses when to halt selection-inference loops, a value function to search over multiple reasoning paths, and sentence labels that help avoid hallucination.
+- [STaR: Bootstrapping Reasoning With Reasoning (2022)](https://arxiv.org/abs/2203.14465): Chain of thought reasoning can be baked into models via fine-tuning. For tasks with an answer key, example chains of thoughts can be generated by language models.
+- [ReAct: Synergizing Reasoning and Acting in Language Models (2023)](https://arxiv.org/abs/2210.03629): For tasks with tools or an environment, chain of thought works better you prescriptively alternate between **Re**asoning steps (thinking about what to do) and **Act**ing (getting information from a tool or environment).
+- [Reflexion: an autonomous agent with dynamic memory and self-reflection (2023)](https://arxiv.org/abs/2303.11366): Retrying tasks with memory of prior failures improves subsequent performance.
+- [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP (2023)](https://arxiv.org/abs/2212.14024): Models augmented with knowledge via a "retrieve-then-read" can be improved with multi-hop chains of searches.
+- [Improving Factuality and Reasoning in Language Models through Multiagent Debate (2023)](https://arxiv.org/abs/2305.14325): Generating debates between a few ChatGPT agents over a few rounds improves scores on various benchmarks. Math word problem scores rise from 77% to 85%.