用少样本训练让大模型精准拆解专业问题，提升金融领域问答准确率。
One More Question is Enough, Expert Question Decomposition (EQD) Model for Domain Quantitative Reasoning
- 通过两阶段微调+奖励机制，自动生成有效子问题。
- 在金融数据集上提升准确率0.6%至10.5%，超越主流方法。
- 只需少量数据和单张显卡，适合快速部署到专业领域。
领域特定的定量推理仍是大型语言模型（LLMs）的重大挑战，尤其在需要专家知识与复杂问答的领域。本文提出专家问题分解（EQD）方法，旨在平衡领域知识利用与计算效率。EQD基于两阶段微调框架，并由衡量生成子问题对问答效果提升程度的奖励函数引导。仅需数千个训练样本和一张A100 GPU即可完成微调，推理时间与零样本提示相当。在金融领域四个基准数据集上的评估显示，该方法在不同LLM上持续提升问答性能0.6%至10.5%。分析揭示：在领域问答中，一个支撑性问题往往比详细步骤指导带来更大收益。
原文摘要 · Abstract (English)
Domain-specific quantitative reasoning remains a major challenge for large language models (LLMs), especially in fields requiring expert knowledge and complex question answering (QA). In this work, we propose Expert Question Decomposition (EQD), an approach designed to balance the use of domain knowledge with computational efficiency. EQD is built on a two-step fine-tuning framework and guided by a reward function that measures the effectiveness of generated sub-questions in improving QA outcomes. It requires only a few thousand training examples and a single A100 GPU for fine-tuning, with inference time comparable to zero-shot prompting. Beyond its efficiency, EQD outperforms state-of-the-art domain-tuned models and advanced prompting strategies. We evaluate EQD in the financial domain, characterized by specialized knowledge and complex quantitative reasoning, across four benchmark datasets. Our method consistently improves QA performance by 0.6% to 10.5% across different LLMs. Our analysis reveals an important insight: in domain-specific QA, a single supporting question often provides greater benefit than detailed guidance steps.
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