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在使用语义的大型语言模型中检测幻觉

Sebastian Farquhar¹, Jannik Kossen², Lorenz Kuhn²

¹OATML, Department of Computer Science, University of Oxford, Oxford, UK. sebfar@gmail.com.

|June 19, 2024

概括

研究人员开发了一种新的统计方法来检测大语言模型 (LLM) 中的一种幻觉. 这种方法通过测量语义层面的不确定性来识别不可靠的AI输出,从而提高AI的可靠性.

科学领域:

人工智能
自然语言处理
机器学习

背景情况:

大型语言模型 (LLM) 具有先进的功能,但患有"幻觉",产生虚假或无根据的信息.
这些不准确性阻碍了法律,新闻和医学等关键领域的LLM应用,
确保LLM真实性的现有方法,如监督或强化学习,只取得了部分成功.

研究的目的:

开发一种通用且可靠的方法来检测LLM的幻觉,特别是说谎.
应对新奇或未见问题的幻觉检测挑战, 人类的答案可能不容易获得.
提高LLM输出的可靠性和可信度,以便更广泛地应用.

主要方法:

开发了基于的不确定性估计的新统计方法.
而不是特定的单词序列,以捕捉思想表达的可变性.
在不同数据集和任务中验证了方法的性能,而无需特定任务的数据.

主要成果:

提出的方法有效地检测出共,
这种方法证明了对新的未见任务和数据集的稳定性和通用性.
语义层面的不确定性估计对于准确的幻觉检测至关重要.

结论:

开发的方法提供了一种可靠的方式来识别潜在的LLM输出.
这项技术使用户能够识别何时要对LLM生成的内容保持谨慎.
这些发现为在各个领域更安全,更可靠地部署LLM铺平了道路.

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