Computer Science > Computation and Language
[Submitted on 10 Sep 2021 (v1), last revised 12 Jan 2022 (this version, v2)]
Title:Entity-Based Knowledge Conflicts in Question Answering
View PDFAbstract:Knowledge-dependent tasks typically use two sources of knowledge: parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts, where the contextual information contradicts the learned information. Analyzing the behaviour of popular models, we measure their over-reliance on memorized information (the cause of hallucinations), and uncover important factors that exacerbate this behaviour. Lastly, we propose a simple method to mitigate over-reliance on parametric knowledge, which minimizes hallucination, and improves out-of-distribution generalization by 4%-7%. Our findings demonstrate the importance for practitioners to evaluate model tendency to hallucinate rather than read, and show that our mitigation strategy encourages generalization to evolving information (i.e., time-dependent queries). To encourage these practices, we have released our framework for generating knowledge conflicts.
Submission history
From: Kartik Perisetla [view email][v1] Fri, 10 Sep 2021 18:29:44 UTC (3,271 KB)
[v2] Wed, 12 Jan 2022 02:41:09 UTC (3,272 KB)
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