Problem
Research questions and friction points this paper is trying to address.
WiC
lexical ambiguity
sense inventory
WiC is Not WSD: A Study on LLMs and Lexical Ambiguity Resolution
🤖 AI Summary
研究探讨了大型语言模型在处理词义消歧时面临的挑战，通过提供候选词义信息改善了WiC任务的表现，减少了因语义粒度不匹配导致的错误。
📝 Abstract
Word-in-Context (WiC) remains challenging for language models, despite recent progress on lexical-semantic tasks. We hypothesise that this difficulty arises not only from comparing two contextual uses of a word, but also from the absence of an explicit sense inventory that specifies the relevant level of semantic granularity. We evaluate open LLMs on WiC and traditional Word Sense Disambiguation (WSD) under similar settings. We find that providing candidate senses, similar to what is done in traditional WSD, improves WiC performance in all settings. In general, explicit sense information helps models make more consistent and targeted judgements. Human evaluation further shows that many apparent WiC errors reflect label ambiguity or mismatches between model and annotator sense boundaries rather than simple failures of lexical understanding. In particular, results show that LLMs overthink the sense distinction often leading to errors based on overly fine-grained distinctions.
Innovation
Methods, ideas, or system contributions that make the work stand out.
sense inventory
lexical ambiguity resolution
LLMs
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