Work

Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence

Year: 2022

Type: conference-paper

Abstract: 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.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/2022.emnlp-main.146

Authors: Hung-Ting Chen, Michael Zhang, Eunsol Choi

Institution: The University of Texas at Austin

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: University of Texas at Austin

OpenAlex ID: W4385574183