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76 lines
2.9 KiB
Markdown
# Concepts
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These are concepts and terminology commonly used when developing LLM applications.
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It contains reference to external papers or sources where the concept was first introduced,
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as well as to places in LangChain where the concept is used.
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## Chain of Thought
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`Chain of Thought (CoT)` is a prompting technique used to encourage the model to generate a series of intermediate reasoning steps.
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A less formal way to induce this behavior is to include “Let’s think step-by-step” in the prompt.
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- [Chain-of-Thought Paper](https://arxiv.org/pdf/2201.11903.pdf)
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- [Step-by-Step Paper](https://arxiv.org/abs/2112.00114)
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## Action Plan Generation
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`Action Plan Generation` is a prompting technique that uses a language model to generate actions to take.
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The results of these actions can then be fed back into the language model to generate a subsequent action.
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- [WebGPT Paper](https://arxiv.org/pdf/2112.09332.pdf)
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- [SayCan Paper](https://say-can.github.io/assets/palm_saycan.pdf)
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## ReAct
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`ReAct` is a prompting technique that combines Chain-of-Thought prompting with action plan generation.
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This induces the model to think about what action to take, then take it.
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- [Paper](https://arxiv.org/pdf/2210.03629.pdf)
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- [LangChain Example](../modules/agents/agents/examples/react.ipynb)
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## Self-ask
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`Self-ask` is a prompting method that builds on top of chain-of-thought prompting.
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In this method, the model explicitly asks itself follow-up questions, which are then answered by an external search engine.
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- [Paper](https://ofir.io/self-ask.pdf)
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- [LangChain Example](../modules/agents/agents/examples/self_ask_with_search.ipynb)
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## Prompt Chaining
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`Prompt Chaining` is combining multiple LLM calls, with the output of one-step being the input to the next.
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- [PromptChainer Paper](https://arxiv.org/pdf/2203.06566.pdf)
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- [Language Model Cascades](https://arxiv.org/abs/2207.10342)
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- [ICE Primer Book](https://primer.ought.org/)
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- [Socratic Models](https://socraticmodels.github.io/)
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## Memetic Proxy
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`Memetic Proxy` is encouraging the LLM
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to respond in a certain way framing the discussion in a context that the model knows of and that
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will result in that type of response.
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For example, as a conversation between a student and a teacher.
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- [Paper](https://arxiv.org/pdf/2102.07350.pdf)
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## Self Consistency
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`Self Consistency` is a decoding strategy that samples a diverse set of reasoning paths and then selects the most consistent answer.
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Is most effective when combined with Chain-of-thought prompting.
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- [Paper](https://arxiv.org/pdf/2203.11171.pdf)
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## Inception
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`Inception` is also called `First Person Instruction`.
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It is encouraging the model to think a certain way by including the start of the model’s response in the prompt.
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- [Example](https://twitter.com/goodside/status/1583262455207460865?s=20&t=8Hz7XBnK1OF8siQrxxCIGQ)
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## MemPrompt
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`MemPrompt` maintains a memory of errors and user feedback, and uses them to prevent repetition of mistakes.
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- [Paper](https://memprompt.com/)
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