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Mahmud Omar1, Benjamin S Glicksberg2, Girish N Nadkarni1

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科学领域:

  • 人工智能的人工智能
  • 计算语言学 计算语言学
  • 医疗信息学 医疗信息学

背景情况:

  • 大型语言模型 (LLM) 在复杂的任务中显示出潜力,比如医疗问题解答.
  • 然而,LLM的绩效增长可能会停滞不前,可靠性仍然是一个问题.
  • 现有的方法可能需要专门的模型或复杂的融合技术.

研究的目的:

  • 引入和评估代共识集 (ICE) 框架,以提高LLM的准确性和可靠性.
  • 评估ICE在各种数据集中的有效性,包括医学和博士级推理任务.
  • 展示一种成本效益高的方法来提高LLM的性能,而无需专门的组件.

主要方法:

  • 开发了Iterative Consensus Ensemble (ICE) 框架,利用多个LLM之间的代推理和反.
  • 从初级保健考试,医疗基准和博士级推理数据集中对4000多个多选题进行了ICE测试.
  • 采用标准的LLM和重复提示,避免专门的奖励模型或代币级融合.

主要成果:

  • 与单个模型尝试相比,ICE的整体准确性提高了高达27%.
  • 在医疗子集上达到81%的准确率,在多域任务上达到72%.
  • 显著提高了GPQA-钻石基准的表现,从46.9%提高到68.2% (超过45%的相对收益).

结论:

  • 通过ICE,LLM之间的代协作提高了推理任务的可靠性和准确性.
  • 对于复杂的模型,ICE提供了一个具有成本效益的替代方案,在专门的数据集上取得可比的结果.
  • 该框架显示出在医学和一般推理领域推进LLM能力的前景.