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        <title>(Chat)GPT v BERT Dawn of Justice for Semantic Change Detection</title>
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    <abstract>In the universe of Natural Language Processing, Transformer-based language models like BERT and (Chat)GPT have emerged as lexical superheroes with great power to solve open research problems. In this paper, we specifically focus on the temporal problem of semantic change, and evaluate their ability to solve two diachronic extensions of the Word-in-Context (WiC) task: TempoWiC and HistoWiC. In particular, we investigate the potential of a novel, off-the-shelf technology like ChatGPT (and GPT) 3.5 compared to BERT, which represents a family of models that currently stand as the state-of-the-art for modeling semantic change. Our experiments represent the first attempt to assess the use of (Chat)GPT for studying semantic change. Our results indicate that ChatGPT performs significantly worse than the foundational GPT version. Furthermore, our results demonstrate that (Chat)GPT achieves slightly lower performance than BERT in detecting long-term changes but performs significantly worse in detecting short-term changes.</abstract>
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%0 Conference Proceedings
%T (Chat)GPT v BERT Dawn of Justice for Semantic Change Detection
%A Periti, Francesco
%A Dubossarsky, Haim
%A Tahmasebi, Nina
%Y Graham, Yvette
%Y Purver, Matthew
%S Findings of the Association for Computational Linguistics: EACL 2024
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F periti-etal-2024-chat
%X In the universe of Natural Language Processing, Transformer-based language models like BERT and (Chat)GPT have emerged as lexical superheroes with great power to solve open research problems. In this paper, we specifically focus on the temporal problem of semantic change, and evaluate their ability to solve two diachronic extensions of the Word-in-Context (WiC) task: TempoWiC and HistoWiC. In particular, we investigate the potential of a novel, off-the-shelf technology like ChatGPT (and GPT) 3.5 compared to BERT, which represents a family of models that currently stand as the state-of-the-art for modeling semantic change. Our experiments represent the first attempt to assess the use of (Chat)GPT for studying semantic change. Our results indicate that ChatGPT performs significantly worse than the foundational GPT version. Furthermore, our results demonstrate that (Chat)GPT achieves slightly lower performance than BERT in detecting long-term changes but performs significantly worse in detecting short-term changes.
%U https://aclanthology.org/2024.findings-eacl.29
%P 420-436

Markdown (Informal)

[(Chat)GPT v BERT Dawn of Justice for Semantic Change Detection](https://aclanthology.org/2024.findings-eacl.29) (Periti et al., Findings 2024)

• (Chat)GPT v BERT Dawn of Justice for Semantic Change Detection (Periti et al., Findings 2024)

ACL

• Francesco Periti, Haim Dubossarsky, and Nina Tahmasebi. 2024. (Chat)GPT v BERT Dawn of Justice for Semantic Change Detection. In Findings of the Association for Computational Linguistics: EACL 2024, pages 420–436, St. Julian’s, Malta. Association for Computational Linguistics.