一个信息理论模型的绑架用于检测幻觉在解释中的解释
1Knowledge Trail Inc., San Jose, CA 95127, USA.
Entropy (Basel, Switzerland)
|February 27, 2026
概括
我们开发了一个神经象征性AI框架,用于检测生成模型中的幻觉. 这种方法使用信息理论和诱导推理来识别未经支持的说法,提高AI的准确性和可解释性.
科学领域:
- 人工智能的人工智能
- 计算语言学 计算语言学
- 信息理论 信息理论
背景情况:
- 生成模型,像大型语言模型 (LLM),可以产生不支持或矛盾的内容,称为幻觉.
- 现有的幻觉检测方法往往缺乏解释性或难以进行复杂的推理.
研究的目的:
- 引入一种新的神经符号框架,用于在生成模型中检测幻觉.
- 为了提高幻觉检测的准确性和解释性.
主要方法:
- 开发了一个信息理论绑架模型 (ITMA).
- ITMA 结合了基于的推理与绑架推理.
- 采用修辞结构理论 (RST) 衍生的EDU权重来构建话语结构.
主要成果:
- 拟议的方法在医疗,事实质量保证和多节点推理数据集上的最先进的神经和象征基线中表现出色.
- 在幻觉检测中表现出卓越的准确性和解释性.
- 成功识别了听起来很合理,但却无法支持的模型错误.
结论:
- 整合信息 - - 理论分歧和诱导性解释为强有力的幻觉检测提供了一个原则性的基础.
- 该框架有效地区分了合法阐述和不合理的要求.
- 为可靠的生成人工智能系统提供了重大进步.
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