AmbigDocs: Reasoning across Documents on Different Entities under the Same Name

Yoonsang Lee, Xi Ye, Eunsol Choi

Research Area: Data, Evaluation, LMs and the world

Keywords: multi-document reasoning, entity disambiguation, ambiguous QA

TL;DR: We study the reasoning capabilities of language models across multiple documents featuring confusing entities.

Abstract:

Different entities with the same name can be difficult to distinguish. Handling confusing entity mentions is a crucial skill for language models (LMs). For example, given the question “Where was Michael Jordan educated?” and a set of documents discussing different people named Michael Jordan, can LMs distinguish entity mentions to generate a cohesive answer to the question? To test this ability, we introduce a new benchmark, AmbigDocs. By leveraging Wikipedia’s disambiguation pages, we identify a set of documents, belonging to different entities who share an ambiguous name. From these documents, we generate questions containing an ambiguous name
and their corresponding sets of answers. Our analysis reveals that current state-of-the-art models often yield ambiguous answers or incorrectly merge information belonging to different entities. We establish an ontology categorizing four types of incomplete answers and automatic evaluation metrics to identify such categories. We lay the foundation for future work on reasoning across multiple documents with ambiguous entities.

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Submission Number: 1368