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        <title>Explaining novel senses using definition generation with open language models</title>
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    <name type="personal">
        <namePart type="given">Mariia</namePart>
        <namePart type="family">Fedorova</namePart>
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    <name type="personal">
        <namePart type="given">Andrey</namePart>
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    <name type="personal">
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    <name type="personal">
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            <namePart type="given">Christos</namePart>
            <namePart type="family">Christodoulopoulos</namePart>
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            <namePart type="given">Tanmoy</namePart>
            <namePart type="family">Chakraborty</namePart>
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    <abstract>We apply definition generators based on open-weights large language models to the task of creating explanations of novel senses, taking target word usages as an input. To this end, we employ the datasets from the AXOLOTL’24 shared task on explainable semantic change modeling, which features Finnish, Russian and German languages. We fine-tune and provide publicly the open-source models performing higher than the best submissions of the aforementioned shared task, which employed closed proprietary LLMs. In addition, we find that encoder-decoder definition generators perform on par with their decoder-only counterparts.</abstract>
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    <identifier type="doi">10.18653/v1/2025.findings-emnlp.1214</identifier>
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%0 Conference Proceedings
%T Explaining novel senses using definition generation with open language models
%A Fedorova, Mariia
%A Kutuzov, Andrey
%A Periti, Francesco
%A Scherrer, Yves
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F fedorova-etal-2025-explaining
%X We apply definition generators based on open-weights large language models to the task of creating explanations of novel senses, taking target word usages as an input. To this end, we employ the datasets from the AXOLOTL’24 shared task on explainable semantic change modeling, which features Finnish, Russian and German languages. We fine-tune and provide publicly the open-source models performing higher than the best submissions of the aforementioned shared task, which employed closed proprietary LLMs. In addition, we find that encoder-decoder definition generators perform on par with their decoder-only counterparts.
%R 10.18653/v1/2025.findings-emnlp.1214
%U https://aclanthology.org/2025.findings-emnlp.1214/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1214
%P 22294-22302

Markdown (Informal)

[Explaining novel senses using definition generation with open language models](https://aclanthology.org/2025.findings-emnlp.1214/) (Fedorova et al., Findings 2025)

• Explaining novel senses using definition generation with open language models (Fedorova et al., Findings 2025)

ACL

• Mariia Fedorova, Andrey Kutuzov, Francesco Periti, and Yves Scherrer. 2025. Explaining novel senses using definition generation with open language models. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 22294–22302, Suzhou, China. Association for Computational Linguistics.