论文标题
丰富的知识来源带来复杂的知识冲突：重新校准模型以反映相互矛盾的证据
Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence
论文作者
论文摘要
问题回答模型可以使用丰富的知识来源 - 在大规模语言模型（LM）中，最多可检索的段落和参数知识。先前的工作假设在这种知识来源中的信息彼此一致，很少关注模型如何将存储在其LM参数中的信息与从检索的证据文件中融合在一起。在本文中，我们模拟了知识冲突（即参数知识暗示一个答案，而不同的段落建议不同的答案）并检查模型行为。我们发现检索性能很大程度上影响了这些源模型的依赖，并且当前的模型主要依赖于其最佳性能的非参数知识。我们发现了一个令人不安的趋势，即知识源之间的矛盾仅会对模型的信心略微影响。为了解决这个问题，我们提出了一项新的校准研究，在其中在检索到的证据中呈现多个相互冲突的答案候选者时，劝阻模型不介绍任何一个答案。
Question answering models can use rich knowledge sources -- up to one hundred retrieved passages and parametric knowledge in the large-scale language model (LM). Prior work assumes information in such knowledge sources is consistent with each other, paying little attention to how models blend information stored in their LM parameters with that from retrieved evidence documents. In this paper, we simulate knowledge conflicts (i.e., where parametric knowledge suggests one answer and different passages suggest different answers) and examine model behaviors. We find retrieval performance heavily impacts which sources models rely on, and current models mostly rely on non-parametric knowledge in their best-performing settings. We discover a troubling trend that contradictions among knowledge sources affect model confidence only marginally. To address this issue, we present a new calibration study, where models are discouraged from presenting any single answer when presented with multiple conflicting answer candidates in retrieved evidences.
