LangChain Glossary

LangChain Glossary Glossary — 🦜🔗 LangChain 0.0.120

• Chain of Thought Prompting

  • A prompting technique used to encourage the model to generate a series of intermediate reasoning steps. A less formal way to induce this behavior is to include “Let’s think step-by-step” in the prompt.
  • Chain of Thought
  • Step-by-Step(LLM)

• Action Plan Generation

  • アクションプラン生成
  • A prompt usage that uses a language model to generate actions to take. The results of these actions can then be fed back into the language model to generate a subsequent action.
  • Resources:
    • WebGPT Paper
    • SayCan Paper

• ReAct Prompting

  • Chain of Thoughtにアクションプラン生成を組み合わせたもの
  • A prompting technique that combines Chain-of-Thought prompting with action plan generation. This induces the to model to think about what action to take, then take it.
  • ReAct(LLM)
  • LangChain Example

• Self-ask

  • 明示的に質問文を生成させ、外部の検索エンジンを使う
  • A prompting method that builds on top of chain-of-thought prompting. In this method, the model explicitly asks itself follow-up questions, which are then answered by an external search engine.
  • Resources:
    • self-ask
    • LangChain Example

• Prompt Chaining

  • Combining multiple LLM calls together, with the output of one-step being the input to the next.
  • Resources:
    • PromptChainer Paper
    • Language Model Cascades
    • ICE Primer Book
    • Socratic Models

• Memetic Proxy

  • 役割を演じさせることで特定文脈での振る舞いをさせる
  • Encouraging the LLM to respond in a certain way framing the discussion in a context that the model knows of and that will result in that type of response. For example, as a conversation between a student and a teacher.
  • Resources:
    • Paper

• Self Consistency

  • A decoding strategy that samples a diverse set of reasoning paths and then selects the most consistent answer. Is most effective when combined with Chain-of-thought prompting.
  • Resources:
    • Paper

• Inception

  • Also called “First Person Instruction”. Encouraging the model to think a certain way by including the start of the model’s response in the prompt.
  • Resources:
  • Example

• MemPrompt

  • MemPrompt maintains a memory of errors and user feedback, and uses them to prevent repetition of mistakes.
  • Resources:
  • Paper