Abstract
In this study, we propose using the GPT-3 as a query generator for the backend of CLIP as an implicit word sense disambiguation (WSD) component for the SemEval 2023 shared task Visual Word Sense Disambiguation (VWSD). We confirmed previous findings - human-like prompts adapted for WSD with quotes benefit both CLIP and GPT-3, whereas plain phrases or poorly templated prompts yield the worst results. Our code is available at https:// github.com/pxm427/WSD-for-IR.
Cite
CITATION STYLE
Pan, X., Chen, Z., & Komachi, M. (2023). Query Generation Using GPT-3 for CLIP-Based Word Sense Disambiguation for Image Retrieval. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 417–422). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.starsem-1.36
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