amazon-science/auto-cot

Health v2 · maintenance only

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2046 stars · 192 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official implementation of Auto-CoT (ICLR 2023), a method that automatically constructs chain-of-thought demonstrations for large language models by sampling diverse questions and generating reasoning chains. It eliminates manual prompt demonstration design while matching or exceeding manual CoT performance on GPT-3 across ten benchmark reasoning tasks.

Use cases

• automatically build chain-of-thought prompt demonstrations for GPT-3
• avoid hand-crafting few-shot reasoning examples for LLM prompting
• reproduce the Auto-CoT ICLR 2023 paper results
• improve LLM accuracy on multi-step reasoning benchmarks like MultiArith
• generate diverse question clusters with reasoning chains for prompting
• compare zero-shot CoT vs automatic few-shot CoT prompting

When to choose

• you need chain-of-thought demonstrations without manual prompt engineering
• you are doing research on LLM prompting and reasoning chains
• you want to replicate or extend the Auto-CoT paper on GPT-3

When to avoid

• you need a production LLM application framework with APIs for modern models
• you want maintained tooling - the repo is a research artifact with limited updates
• you work with models other than GPT-3-era APIs without adaptation

Facets

library · maturity maintenance

prompt-engineering machine-learning nlp large-language-models artificial-intelligence machine-learning python chain-of-thought gpt-3 llm-prompting reasoning research-code iclr-2023 natural-language-processing

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