陷入了词汇网络:在医学文献中,LLMs是否会陷入困境?
Hye Sun Yun1, Karen Y C Zhang1, Ramez Kouzy2
1Northeastern University, Boston, MA, USA.
概括
大型语言模型 (LLM) 与人类相比,对研究结果旋转的敏感性增加. 提示可以帮助减轻LLM输出中的这种偏差.
科学领域:
- 医学研究 医学研究
- 人工智能的人工智能
- 临床翻译 临床翻译
背景情况:
- 医学研究翻译面临挑战,出版激励有利于积极的结果.
- 作者经常"扭曲"研究结果,特别是在摘要中,可能会影响临床决策.
- 大型语言模型 (LLM) 越来越多地用于合成医学证据.
研究的目的:
- 调查LLM是否容易导致医学研究摘要中的转折.
- 为了确定LLM是否传播旋转到它们的合成输出中,例如普通语言摘要.
主要方法:
- 评估22种不同的大型语言模型 (LLM).
- 与人类解释相比,医学研究摘要中的LLM易受"旋转"的评估.
- 对LLM生成的简单语言摘要进行分析,以实现隐式旋转的整合.
主要成果:
- 总体而言,LLM表现出比人类评估者更容易受到结果旋转的影响.
- 有证据表明,LLM可以隐性地将旋转纳入生成的普通语言摘要中.
- 在适当的提示下,LLM通常具有识别旋转的能力.
结论:
- 比起人类,LLM对研究结果的转换更容易受到影响.
- 在合成的医疗信息中,LLM可能会传播旋转,影响证据的解释.
- 战略提示可以增强LLM减轻旋转对其输出的影响的能力.
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