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        <title>Can LLMs be Literary Companions?: Analysing LLMs on Bengali Figures of Speech Identification</title>
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    <abstract>Despite Bengali being among the most spoken languages bearing cultural importance and richness, the NLP endeavors on it, remain relatively limited. Figures of Speech (FoS) not only contribute to the phonetic and semantic nuances of a language, but they also exhibit aesthetics, expression, and creativity in literature. To our knowledge, in this paper, we present the first ever Bengali figures of speech classification dataset, **BengFoS**, on works of six renowned poets of Bengali literature. We deploy state-of-the-art Large Language Models (LLMs) to this dataset in the zero-shot setup, thereafter fine-tuning the best performing models, and finally dissect them for language model probing. This reveals novel insights on the intrinsic behavior of two open-source LLMs (Llama and DeepSeek) in FoS detection. **Though we have limited ourselves to Bengali, the experimental framework can be reproduced for English as well as for other low-resource languages**.</abstract>
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%0 Conference Proceedings
%T Can LLMs be Literary Companions?: Analysing LLMs on Bengali Figures of Speech Identification
%A Das, Sourav
%A Ghosh, Kripabandhu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F das-ghosh-2025-llms
%X Despite Bengali being among the most spoken languages bearing cultural importance and richness, the NLP endeavors on it, remain relatively limited. Figures of Speech (FoS) not only contribute to the phonetic and semantic nuances of a language, but they also exhibit aesthetics, expression, and creativity in literature. To our knowledge, in this paper, we present the first ever Bengali figures of speech classification dataset, **BengFoS**, on works of six renowned poets of Bengali literature. We deploy state-of-the-art Large Language Models (LLMs) to this dataset in the zero-shot setup, thereafter fine-tuning the best performing models, and finally dissect them for language model probing. This reveals novel insights on the intrinsic behavior of two open-source LLMs (Llama and DeepSeek) in FoS detection. **Though we have limited ourselves to Bengali, the experimental framework can be reproduced for English as well as for other low-resource languages**.
%U https://aclanthology.org/2025.emnlp-main.941/
%P 18645-18667

Markdown (Informal)

[Can LLMs be Literary Companions?: Analysing LLMs on Bengali Figures of Speech Identification](https://aclanthology.org/2025.emnlp-main.941/) (Das & Ghosh, EMNLP 2025)

• Can LLMs be Literary Companions?: Analysing LLMs on Bengali Figures of Speech Identification (Das & Ghosh, EMNLP 2025)

ACL

• Sourav Das and Kripabandhu Ghosh. 2025. Can LLMs be Literary Companions?: Analysing LLMs on Bengali Figures of Speech Identification. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 18645–18667, Suzhou, China. Association for Computational Linguistics.