ACIL: Auto Chain of Thoughts for In-Context Learning

TL;DR

Auto-CoT enhances in-context learning by automatically generating and selecting high-quality reasoning chains, leading to improved performance on complex reasoning tasks with large language models.

Contribution

The paper introduces Auto-CoT, a novel framework that automatically constructs and selects reasoning-enhanced demonstrations to improve in-context learning.

Findings

Auto-CoT significantly improves reasoning accuracy across multiple tasks.

Automatically generated reasoning chains enhance model performance.

Systematic selection filters out low-quality demonstrations.

Abstract

Recent advances in large language models (LLMs) have shown that Chain-of-Thought (CoT) reasoning can substantially improve performance on complex reasoning tasks. At the same time, In-Context Learning (ICL) has become an important mechanism for adapting LLMs to new tasks without updating model parameters, using only examples provided in the prompt. However, standard ICL often struggles on tasks that require multi-step reasoning, because the demonstrations usually contain only input-output pairs and lack explicit intermediate reasoning steps. This paper introduces an Automatic Chain-of-Thought (Auto-CoT) framework to improve ICL by automatically constructing reasoning-enhanced demonstrations. Auto-CoT generates reasoning chains for input-output examples, augments the prompt context with structured intermediate explanations, and removes irrelevant or low-quality demonstrations through a systematic selection process. By incorporating high-quality reasoning examples into the ICL prompt, Auto-CoT guides the model toward more reliable reasoning and improves prediction accuracy. Experiments across multiple reasoning tasks demonstrate that the proposed framework improves ICL performance by providing explicit intermediate reasoning guidance.