用专业大模型和检索技术，让生物医学研究问答更准更快。
Streamlining Biomedical Research with Specialized LLMs
- 结合领域大模型与信息检索，实现跨模块协同验证。
- 问答准确率显著提升，支持图文多模态响应。
- 适合医药研发人员快速获取文献与数据，提速决策。
本文提出一种新系统，将前沿的领域专用大语言模型与先进信息检索技术融合，提供全面且上下文感知的回应。该方法促进各组件间无缝交互，通过输出交叉验证生成高精度、高质量的回答，并整合相关数据、图像、表格等多模态内容。实验表明，系统通过强大的问答模型显著提升了回答精度，大幅改善对话生成质量。平台支持实时、高保真交互，让用户高效获取文献与数据，实现精准检索与人机协作。显著提升生物医药领域研究人员的工作效率，加速研发全过程中的决策进程。系统已上线：https://synapse-chat.patsnap.com。
原文摘要 · Abstract (English)
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.
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