Streamlining Biomedical Research with Specialized LLMs
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arXiv
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2025
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| author | Chen, Linqing Wang, Weilei Xia, Yubin Wu, Wentao Xu, Peng Bai, Zilong Fang, Jie Xu, Chaobo Hu, Ran Xu, Licong Hua, Haoran Sun, Jing Zhong, Hanmeng Liu, Jin Qiu, Tian Liu, Haowen Hu, Meng Li, Xiuwen Gao, Fei Gu, Yong Shi, Tao Wang, Chaochao Lu, Jianping Sun, Cheng Wang, Yixin Yang, Shengjie Li, Yuancheng Jin, Lu Zhang, Lisha Bian, Fu Ye, Zhongkai Pei, Lidong Tu, Changyang |
| author_facet | Chen, Linqing Wang, Weilei Xia, Yubin Wu, Wentao Xu, Peng Bai, Zilong Fang, Jie Xu, Chaobo Hu, Ran Xu, Licong Hua, Haoran Sun, Jing Zhong, Hanmeng Liu, Jin Qiu, Tian Liu, Haowen Hu, Meng Li, Xiuwen Gao, Fei Gu, Yong Shi, Tao Wang, Chaochao Lu, Jianping Sun, Cheng Wang, Yixin Yang, Shengjie Li, Yuancheng Jin, Lu Zhang, Lisha Bian, Fu Ye, Zhongkai Pei, Lidong Tu, Changyang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12341 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Streamlining Biomedical Research with Specialized LLMs Chen, Linqing Wang, Weilei Xia, Yubin Wu, Wentao Xu, Peng Bai, Zilong Fang, Jie Xu, Chaobo Hu, Ran Xu, Licong Hua, Haoran Sun, Jing Zhong, Hanmeng Liu, Jin Qiu, Tian Liu, Haowen Hu, Meng Li, Xiuwen Gao, Fei Gu, Yong Shi, Tao Wang, Chaochao Lu, Jianping Sun, Cheng Wang, Yixin Yang, Shengjie Li, Yuancheng Jin, Lu Zhang, Lisha Bian, Fu Ye, Zhongkai Pei, Lidong Tu, Changyang Computation and Language 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. |
| title | Streamlining Biomedical Research with Specialized LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2504.12341 |