自动分解问题生成推理链，提升小模型的多步推理能力
AutoReason: Automatic Few-Shot Reasoning Decomposition
- 用CoT自动拆解隐含问题为显式子问题
- 在StrategyQA上准确率显著提升，HotpotQA也有所改善
- 适合需要可解释推理的小样本场景研究
思维链（Chain of Thought, CoT）是一种提升大语言模型逐步推理能力的方法。然而，其应用受限于需人工设计少样本示例提示，且无法自适应不同查询。本文提出一种自动生成推理链的系统，通过将隐含问题分解为多个显式子问题，增强模型的多步隐式推理能力，并提升弱小语言模型的可解释性。我们在两个问答数据集StrategyQA和HotpotQA上测试该方法，结果表明准确率均有提升，尤其在StrategyQA上表现更优。为促进后续研究，本文完整代码已开源至GitHub：https://github.com/miralab-ai/autoreason。
原文摘要 · Abstract (English)
Chain of Thought (CoT) was introduced in recent research as a method for improving step-by-step reasoning in Large Language Models. However, CoT has limited applications such as its need for hand-crafted few-shot exemplar prompts and no capability to adjust itself to different queries. In this work, we propose a system to automatically generate rationales using CoT. Our method improves multi-step implicit reasoning capabilities by decomposing the implicit query into several explicit questions. This provides interpretability for the model, improving reasoning in weaker LLMs. We test our approach with two Q\&A datasets: StrategyQA and HotpotQA. We show an increase in accuracy with both, especially on StrategyQA. To facilitate further research in this field, the complete source code for this study has been made publicly available on GitHub: https://github.com/miralab-ai/autoreason.
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