利用LLM集体智能实现稳健的生物医学问答：一种多模型方法
Harnessing Collective Intelligence of LLMs for Robust Biomedical QA: A Multi-Model Approach
AI总结：
本研究提出一种多模型集成方法，利用多数投票和答案并集机制，在BioASQ 2025挑战赛的生物医学问答任务中取得多项第一，验证了LLM组合的有效性。
摘要：
生物医学文本挖掘和问答是至关重要但要求极高的任务，尤其是在生物医学文献呈指数级增长的背景下。在这项工作中，我们展示了我们参加第13届BioASQ挑战赛的情况，该挑战赛涉及Task 13b的生物医学语义问答以及Synergy任务中开发主题的生物医学问答。我们部署了一系列开源大型语言模型（LLM）作为检索增强生成器来回答生物医学问题。使用多种模型来处理这些问题。对于是/否问题，采用多数投票系统综合其输出以确定最终答案；而对于列表型和事实型问题，则使用其答案的并集。我们评估了13个最先进的开源LLM，探索了所有可能的模型组合以形成最终答案，从而为每种问题类型定制了LLM流水线。我们的研究结果提供了关于哪些LLM组合在特定问题类型上持续产生更优结果的宝贵见解。在2025年BioASQ挑战赛的四轮比赛中，我们的系统取得了显著成绩：在Synergy任务中，我们在第2轮中获得了理想答案的第一名和精确答案的第二名，并在第3轮和第4轮中两次并列获得精确答案的第一名。
英文摘要：
Biomedical text mining and question-answering are essential yet highly demanding tasks, particularly in the face of the exponential growth of biomedical literature. In this work, we present our participation in the 13th edition of the BioASQ challenge, which involves biomedical semantic question-answering for Task 13b and biomedical question-answering for developing topics for the Synergy task. We deploy a selection of open-source large language models (LLMs) as retrieval-augmented generators to answer biomedical questions. Various models are used to process the questions. A majority voting system combines their output to determine the final answer for Yes/No questions, while for list and factoid type questions, the union of their answers in used. We evaluated 13 state-of-the-art open source LLMs, exploring all possible model combinations to contribute to the final answer, resulting in tailored LLM pipelines for each question type. Our findings provide valuable insight into which combinations of LLMs consistently produce superior results for specific question types. In the four rounds of the 2025 BioASQ challenge, our system achieved notable results: in the Synergy task, we secured 1st place for ideal answers and 2nd place for exact answers in round 2, as well as two shared 1st places for exact answers in round 3 and 4.
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