Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence
This addresses the issue of knowledge conflicts in retrieval-augmented QA systems, which is incremental as it builds on prior work by simulating and mitigating conflicts.
The paper tackles the problem of question answering models blending parametric knowledge with retrieved passages, revealing that models rely heavily on non-parametric knowledge and contradictions among sources only marginally affect confidence. It presents a calibration study to discourage models from presenting a single answer when faced with conflicting evidence.
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.