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    <abstract>In this paper, we propose a novel system that integrates state-of-the-art, domain-specific large language models with advanced information retrieval techniques to deliver comprehensive and context-aware responses. Our approach facilitates seamless interaction among diverse components, enabling cross-validation of outputs to produce accurate, high-quality responses enriched with relevant data, images, tables, and other modalities. We demonstrate the system‘s capability to enhance response precision by leveraging a robust question-answering model, significantly improving the quality of dialogue generation.The system provides an accessible platform for real-time, high-fidelity interactions, allowing users to benefit from efficient human-computer interaction, precise retrieval, and simultaneous access to a wide range of literature and data. This dramatically improves the research efficiency of professionals in the biomedical and pharmaceutical domains and facilitates faster, more informed decision-making throughout the R&amp;D process. Furthermore, the system proposed in this paper is available at https://synapse-chat.patsnap.com.</abstract>
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%0 Conference Proceedings
%T Streamlining Biomedical Research with Specialized LLMs
%A Chen, Linqing
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%Y Mather, Brodie
%Y Dras, Mark
%S Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F chen-2025-streamlining
%X In this paper, we propose a novel system that integrates state-of-the-art, domain-specific large language models with advanced information retrieval techniques to deliver comprehensive and context-aware responses. Our approach facilitates seamless interaction among diverse components, enabling cross-validation of outputs to produce accurate, high-quality responses enriched with relevant data, images, tables, and other modalities. We demonstrate the system‘s capability to enhance response precision by leveraging a robust question-answering model, significantly improving the quality of dialogue generation.The system provides an accessible platform for real-time, high-fidelity interactions, allowing users to benefit from efficient human-computer interaction, precise retrieval, and simultaneous access to a wide range of literature and data. This dramatically improves the research efficiency of professionals in the biomedical and pharmaceutical domains and facilitates faster, more informed decision-making throughout the R&D process. Furthermore, the system proposed in this paper is available at https://synapse-chat.patsnap.com.
%U https://aclanthology.org/2025.coling-demos.2/
%P 9-19

Markdown (Informal)

[Streamlining Biomedical Research with Specialized LLMs](https://aclanthology.org/2025.coling-demos.2/) (Chen, COLING 2025)

• Streamlining Biomedical Research with Specialized LLMs (Chen, COLING 2025)

ACL

• Linqing Chen. 2025. Streamlining Biomedical Research with Specialized LLMs. In Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations, pages 9–19, Abu Dhabi, UAE. Association for Computational Linguistics.