在LLM中寻找共识,以获取有关瘤学试验的信息.
Fabio Dennstädt1,2, Paul Windisch3, Irina Filchenko1
1Inselspital, Bern University Hospital and University of Bern, Switzerland.
Studies in health technology and informatics
|August 8, 2025
概括
使用三种大型语言模型 (LLM) 的共识方法显著提高了分类瘤试验的准确性. 这种方法提高了癌症研究中自动化文献分析的可靠性.
科学领域:
- 在瘤学瘤学.
- 生物医学信息学 生物医学信息学
- 人工智能的人工智能
背景情况:
- 医学文献的自动分类至关重要,特别是在瘤学中.
- 大型语言模型 (LLM) 显示出对生物医学文献和临床试验的分类有希望.
- 之前的研究证实了LLM在这个领域的实用性.
研究的目的:
- 评估基于共识的方法对提高LLM分类性能的有效性.
- 确定共识在多大程度上提高了瘤学试验分类的准确性.
主要方法:
- 他们雇佣了三个LLM (Mixtral-8x7B,Meta-Llama-3.1-70B,Qwen2.5-72B) 的工作人员.
- 对四个数据集进行了瘤学试验的分类,使用了九个不同的问题.
- 性能指标包括准确性,精度,回忆和F1得分,这些指标被评估为单个模型和共识输出.
主要成果:
- 在93.93%的分类任务中达成共识.
- 达成共识的方法产生了优越的性能指标:98.34%的准确性,97.01%的精度,98.11%的回忆力,97.55%的F1得分.
- 这些结果显著超过了个人LLM的表现.
结论:
- 基于共识的LLM框架证明了对分类瘤试验的高准确性和适应性.
- 这种方法有可能促进生物医学研究和临床试验管理.
- 这些发现支持将共识机制纳入人工智能驱动的文献分析.
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