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Entity-Based Knowledge Conflicts in Question Answering

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arxiv 2109.05052 v2 pith:VQWRLNGK submitted 2021-09-10 cs.CL cs.LG

Entity-Based Knowledge Conflicts in Question Answering

Shayne Longpre , Kartik Perisetla , Anthony Chen , Nikhil Ramesh , Chris DuBois , Sameer Singh This is my paper
classification cs.CL cs.LG
keywords knowledgeinformationconflictsbehaviourcontextualgeneralizationlearnedmodels
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 11 Pith papers

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