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    <abstract>Despite their strong linguistic capabilities, Large Language Models (LLMs) are computationally demanding and require substantial resources for fine-tuning, which is unadapted to privacy and budget constraints of many healthcare settings. To address this, we present an experimental analysis focused on Biomedical Named Entity Recognition using lightweight LLMs, we evaluate the impact of different output formats on model performance. The results reveal that lightweight LLMs can achieve competitive performance compared to the larger models, highlighting their potential as lightweight yet effective alternatives for biomedical information extraction. Our analysis shows that instruction tuning over many distinct formats does not improve performance, but identifies several format consistently associated with better performance.</abstract>
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%0 Conference Proceedings
%T Analysing Lightweight Large Language Models for Biomedical Named Entity Recognition on Diverse Ouput Formats
%A Epron, Pierre
%A Coulet, Adrien
%A Alam, Mehwish
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F epron-etal-2026-analysing
%X Despite their strong linguistic capabilities, Large Language Models (LLMs) are computationally demanding and require substantial resources for fine-tuning, which is unadapted to privacy and budget constraints of many healthcare settings. To address this, we present an experimental analysis focused on Biomedical Named Entity Recognition using lightweight LLMs, we evaluate the impact of different output formats on model performance. The results reveal that lightweight LLMs can achieve competitive performance compared to the larger models, highlighting their potential as lightweight yet effective alternatives for biomedical information extraction. Our analysis shows that instruction tuning over many distinct formats does not improve performance, but identifies several format consistently associated with better performance.
%R 10.63317/3kdyx7mu3dib
%U https://aclanthology.org/2026.lrec-1.193/
%U https://doi.org/10.63317/3kdyx7mu3dib
%P 2458-2470

Markdown (Informal)

[Analysing Lightweight Large Language Models for Biomedical Named Entity Recognition on Diverse Ouput Formats](https://aclanthology.org/2026.lrec-1.193/) (Epron et al., LREC 2026)

• Analysing Lightweight Large Language Models for Biomedical Named Entity Recognition on Diverse Ouput Formats (Epron et al., LREC 2026)

ACL

• Pierre Epron, Adrien Coulet, and Mehwish Alam. 2026. Analysing Lightweight Large Language Models for Biomedical Named Entity Recognition on Diverse Ouput Formats. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 2458–2470, Palma de Mallorca, Spain. ELRA Language Resource Association.