mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
75 lines
2.8 KiB
Markdown
75 lines
2.8 KiB
Markdown
# Glossary
|
||
|
||
This is a collection of terminology commonly used when developing LLM applications.
|
||
It contains reference to external papers or sources where the concept was first introduced,
|
||
as well as to places in LangChain where the concept is used.
|
||
|
||
### 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.
|
||
|
||
Resources:
|
||
- [Chain-of-Thought Paper](https://arxiv.org/pdf/2201.11903.pdf)
|
||
- [Step-by-Step Paper](https://arxiv.org/abs/2112.00114)
|
||
|
||
### Action Plan Generation
|
||
|
||
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](https://arxiv.org/pdf/2112.09332.pdf)
|
||
- [SayCan Paper](https://say-can.github.io/assets/palm_saycan.pdf)
|
||
|
||
### ReAct Prompting
|
||
|
||
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.
|
||
|
||
Resources:
|
||
- [Paper](https://arxiv.org/pdf/2210.03629.pdf)
|
||
- [LangChain Example](https://github.com/hwchase17/langchain/blob/master/docs/examples/agents/react.ipynb)
|
||
|
||
### Self-ask
|
||
|
||
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:
|
||
- [Paper](https://ofir.io/self-ask.pdf)
|
||
- [LangChain Example](https://github.com/hwchase17/langchain/blob/master/docs/examples/agents/self_ask_with_search.ipynb)
|
||
|
||
### Prompt Chaining
|
||
|
||
Combining multiple LLM calls together, with the output of one step being the input to the next.
|
||
|
||
Resources:
|
||
- [PromptChainer Paper](https://arxiv.org/pdf/2203.06566.pdf)
|
||
- [Language Model Cascades](https://arxiv.org/abs/2207.10342)
|
||
- [ICE Primer Book](https://primer.ought.org/)
|
||
- [Socratic Models](https://socraticmodels.github.io/)
|
||
|
||
### Memetic Proxy
|
||
|
||
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](https://arxiv.org/pdf/2102.07350.pdf)
|
||
|
||
### 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](https://arxiv.org/pdf/2203.11171.pdf)
|
||
|
||
### 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](https://twitter.com/goodside/status/1583262455207460865?s=20&t=8Hz7XBnK1OF8siQrxxCIGQ)
|