Chain-of-thought prompting

Introduced by Wei et al. in Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Chain-of-thought prompts contain a series of intermediate reasoning steps, and they are shown to significantly improve the ability of large language models to perform certain tasks that involve complex reasoning (e.g., arithmetic, commonsense reasoning, symbolic reasoning, etc.)

Source: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

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Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
Language Modelling	5	9.62%
Question Answering	4	7.69%
In-Context Learning	3	5.77%
Sentence	3	5.77%
Arithmetic Reasoning	3	5.77%
Sentiment Analysis	2	3.85%
Stance Detection	2	3.85%
Prompt Engineering	2	3.85%
Multi-hop Question Answering	2	3.85%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

Components

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Prompt Engineering