用细粒度验证减少金融问答中的幻觉，提升生成内容与检索信息的一致性。
Mitigating Hallucination in Financial Retrieval-Augmented Generation via Fine-Grained Knowledge Verification
- 将金融回答拆解为原子知识单元，逐个验证真伪以优化模型
- 在FDD和FDD-ANT数据集上，幻觉率显著降低，生成更忠实于原文
- 适合需要高可靠性金融生成的场景，如智能投研、合规报告
在金融领域，检索增强生成系统依赖检索文档生成及时准确的回答。然而，模型生成内容仍常出现与检索信息矛盾的幻觉。为此，我们提出基于强化学习的细粒度知识验证框架（RLFKV）。该方法将金融回答分解为原子知识单元，评估每个单元的正确性，从而计算细粒度忠实奖励，提供更精准的优化信号。此外，为防止奖励劫持（如过度简略回复），引入信息量奖励，确保生成内容至少保留基础模型的知识单元数量。在公开的金融数据描述（FDD）任务及新提出的FDD-ANT数据集上的实验表明，本方法持续提升生成质量，有效缓解了幻觉问题。
原文摘要 · Abstract (English)
In financial Retrieval-Augmented Generation (RAG) systems, models frequently rely on retrieved documents to generate accurate responses due to the time-sensitive nature of the financial domain. While retrieved documents help address knowledge gaps, model-generated responses still suffer from hallucinations that contradict the retrieved information. To mitigate this inconsistency, we propose a Reinforcement Learning framework enhanced with Fine-grained Knowledge Verification (RLFKV). Our method decomposes financial responses into atomic knowledge units and assesses the correctness of each unit to compute the fine-grained faithful reward. This reward offers more precise optimization signals, thereby improving alignment with the retrieved documents. Additionally, to prevent reward hacking (e.g., overly concise replies), we incorporate an informativeness reward that encourages the policy model to retain at least as many knowledge units as the base model. Experiments conducted on the public Financial Data Description (FDD) task and our newly proposed FDD-ANT dataset demonstrate consistent improvements, confirming the effectiveness of our approach.
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