用大模型+检索增强打造植物光合研究助手，提升科研写作与知识整合能力
Knowledge Synthesis of Photosynthesis Research Using a Large Language Model
- 基于GPT-4o与RAG技术构建光合研究助手PRAG，优化提示词并引入反馈机制
- 在科学写作等五项指标上平均提升8.7%，来源透明度提高25.4%，接近论文水平
- 通过知识图谱结构化输出，可精准匹配63%数据库文献与39.5%外部论文关键实体
生物数据分析工具与大语言模型的发展为植物科学中的AI应用开辟了新路径，有助于知识整合与研究空白识别。然而，现有大模型在处理光合研究中的复杂数据与理论模型时仍存在科学上下文不准确的问题。为此，本研究提出基于OpenAI GPT-4o的光合研究助手PRAG，结合检索增强生成（RAG）与提示词优化技术，利用向量数据库和自动化反馈循环提升回答的准确性与相关性。PRAG在五项科学写作指标上平均提升8.7%，来源透明度提高25.4%；其科学深度与领域覆盖范围与光合研究论文相当。通过知识图谱结构化响应，使PRAG能匹配数据库文献中63%、测试论文中39.5%的关键实体。该系统可应用于光合研究及更广泛的植物科学领域，推动更深入的数据分析与预测能力。
原文摘要 · Abstract (English)
The development of biological data analysis tools and large language models (LLMs) has opened up new possibilities for utilizing AI in plant science research, with the potential to contribute significantly to knowledge integration and research gap identification. Nonetheless, current LLMs struggle to handle complex biological data and theoretical models in photosynthesis research and often fail to provide accurate scientific contexts. Therefore, this study proposed a photosynthesis research assistant (PRAG) based on OpenAI's GPT-4o with retrieval-augmented generation (RAG) techniques and prompt optimization. Vector databases and an automated feedback loop were used in the prompt optimization process to enhance the accuracy and relevance of the responses to photosynthesis-related queries. PRAG showed an average improvement of 8.7% across five metrics related to scientific writing, with a 25.4% increase in source transparency. Additionally, its scientific depth and domain coverage were comparable to those of photosynthesis research papers. A knowledge graph was used to structure PRAG's responses with papers within and outside the database, which allowed PRAG to match key entities with 63% and 39.5% of the database and test papers, respectively. PRAG can be applied for photosynthesis research and broader plant science domains, paving the way for more in-depth data analysis and predictive capabilities.
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