diff --git a/README.md b/README.md index f85bad0..27f2e89 100644 --- a/README.md +++ b/README.md @@ -54,21 +54,65 @@ Most code examples are written in Python, though the concepts can be applied in - [How to get embeddings from Azure OpenAI](examples/azure/embeddings.ipynb) - [How to fine-tune GPT-3 with Azure OpenAI](examples/azure/finetuning.ipynb) -## Related resources +## Related OpenAI resources Beyond the code examples here, you can learn about the [OpenAI API] from the following resources: - Experiment with [ChatGPT] -- Try out the API in the [OpenAI Playground] +- Try the API in the [OpenAI Playground] - Read about the API in the [OpenAI Documentation] -- Discuss the API in the [OpenAI Community Forum] -- Look for help in the [OpenAI Help Center] +- Get help in the [OpenAI Help Center] +- Discuss the API in the [OpenAI Community Forum] or [OpenAI Discord channel] - See example prompts in the [OpenAI Examples] -- Stay up to date with the [OpenAI Blog] +- 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://devblogs.microsoft.com/semantic-kernel/): A Python/C# library from Microsoft that supports prompt templating, function chaining, vectorized memory, and intelligent planning. +- [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. + +### Prompting guides + +- [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) go 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. +- [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. + + ## Contributing -If there are examples or guides you'd like to see, feel free to suggest them on the [issues page]. +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. [chatgpt]: https://chat.openai.com/ [openai api]: https://openai.com/api/ @@ -76,7 +120,8 @@ If there are examples or guides you'd like to see, feel free to suggest them on [openai playground]: https://beta.openai.com/playground [openai documentation]: https://beta.openai.com/docs/introduction [openai community forum]: https://community.openai.com/top?period=monthly +[openai discord channel]: https://discord.com/invite/openai [openai help center]: https://help.openai.com/en/ [openai examples]: https://beta.openai.com/examples [openai blog]: https://openai.com/blog/ -[issues page]: https://github.com/openai/openai-cookbook/issues +[issues page]: https://github.com/openai/openai-cookbook/issues \ No newline at end of file