用外部知识和强化学习提升中文问答系统纠错能力
Knowledge-Augmented Question Error Correction for Chinese Question Answer System with QuestionRAG
- 引入外部知识增强有误问题的语义理解
- 强化学习使纠错更精准，减少无谓修改
- 适合需要高可靠问答系统的场景
问答系统中输入错误常导致错误回答。大语言模型在此任务上表现不佳，常出现意图误判或过度修正原问题结构。我们提出QuestionRAG框架：通过外部知识（如搜索结果、相关实体）增强输入以缓解误判；采用强化学习（RL）对齐模型目标，实现精准纠错而非简单改写。实验表明，知识增强对理解有误问题至关重要；基于RL的对齐显著优于传统监督微调（SFT），大幅提升模型指令遵循与泛化能力。二者结合充分释放了大模型在纠错任务中的潜力。
原文摘要 · Abstract (English)
Input errors in question-answering (QA) systems often lead to incorrect responses. Large language models (LLMs) struggle with this task, frequently failing to interpret user intent (misinterpretation) or unnecessarily altering the original question's structure (over-correction). We propose QuestionRAG, a framework that tackles these problems. To address misinterpretation, it enriches the input with external knowledge (e.g., search results, related entities). To prevent over-correction, it uses reinforcement learning (RL) to align the model's objective with precise correction, not just paraphrasing. Our results demonstrate that knowledge augmentation is critical for understanding faulty questions. Furthermore, RL-based alignment proves significantly more effective than traditional supervised fine-tuning (SFT), boosting the model's ability to follow instructions and generalize. By integrating these two strategies, QuestionRAG unlocks the full potential of LLMs for the question correction task.
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