解决大模型问答中的知识冲突，减少幻觉生成。
Mitigating Knowledge Conflicts in Language Model-Driven Question Answering
- 通过建立输入与输出的显式关联，缓解内部知识与外部信息的冲突。
- 实验证明该方法能显著降低问答中因知识错配导致的幻觉现象。
- 适合关注大模型可靠性与生成忠实性的研究人员和开发者。
在基于文档的问答与摘要等知识驱动的序列生成任务中，模型参数内嵌的知识与上下文获取的外部知识是两大关键来源。近期研究发现，当模型内在知识与训练数据中的真实答案存在不一致时，推理阶段可能出现忽略输入上下文或生成不忠实内容等问题。本文提出一种策略，通过构建源输入与生成输出之间的显式联系，有效缓解幻觉问题。重点分析了训练过程中实体与其上下文对应关系对推理性能的影响，揭示了知识对齐的重要性。
原文摘要 · Abstract (English)
In the context of knowledge-driven seq-to-seq generation tasks, such as document-based question answering and document summarization systems, two fundamental knowledge sources play crucial roles: the inherent knowledge embedded within model parameters and the external knowledge obtained through context. Recent studies revealed a significant challenge: when there exists a misalignment between the model's inherent knowledge and the ground truth answers in training data, the system may exhibit problematic behaviors during inference, such as ignoring input context, or generating unfaithful content. Our investigation proposes a strategy to minimize hallucination by building explicit connection between source inputs and generated outputs. We specifically target a common hallucination pattern in question answering, examining how the correspondence between entities and their contexts during model training influences the system's performance at inference time.
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