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| Title | cover Can Large Language Models Compete with Specialized Models in Lexical Semantic Change Detection? |
| Authors | Frank D. Zamora-Reina, Felipe Bravo-Marquez, Dominik Schlechtweg, Nikolay Arefyev |
| Publication date | 2025 |
| Abstract | In this paper, we present a comprehensive comparison between specialized Lexical Semantic Change Detection (LSCD) models and Large Language Models (LLMs) for the LSCD task. In addition to comparing models, we also investigate the role of automatic prompt selection for improving LLM performance. We evaluate three approaches: Average Pairwise Distance (APD), Word-in-Context (WiC), and Word Sense Induction (WSI). Using Spearman correlation as the evaluation metric, we assess the performance of Mixtral, Llama 3.1, Llama 3.3, and specialized LSCD models across English and Spanish datasets. Our results show that by using prompt optimization and LLMs, we achieve state-of-the-art performance for the English dataset and outperform specialized LSCD models at the annotation level in the same dataset. For Spanish, specialized models outperform LLMs across all three approaches--WiC, APD, and WSI--indicating that specialized LSCD models are still more effective for semantic change detection in Spanish. |
| Pages | 4201-4208 |
| Conference name | European Conference on Artificial Intelligence |
| Publisher | IOS Press (Amsterdam, The Netherlands) |
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