SANDWiCH: Semantical Analysis of Neighbours for Disambiguating Words in Context ad Hoc
This addresses the limited contextual understanding of words in large language models, which is crucial for reasoning, with significant gains even in low-resource languages.
The paper tackles the problem of word sense disambiguation (WSD) in multilingual contexts by reframing it as cluster discrimination analysis over a semantic network, achieving new state-of-the-art results across all languages and tasks while reducing parameters by 72%.
The rise of generative chat-based Large Language Models (LLMs) over the past two years has spurred a race to develop systems that promise near-human conversational and reasoning experiences. However, recent studies indicate that the language understanding offered by these models remains limited and far from human-like performance, particularly in grasping the contextual meanings of words, an essential aspect of reasoning. In this paper, we present a simple yet computationally efficient framework for multilingual Word Sense Disambiguation (WSD). Our approach reframes the WSD task as a cluster discrimination analysis over a semantic network refined from BabelNet using group algebra. We validate our methodology across multiple WSD benchmarks, achieving a new state of the art for all languages and tasks, as well as in individual assessments by part of speech. Notably, our model significantly surpasses the performance of current alternatives, even in low-resource languages, while reducing the parameter count by 72%.