在多代理系统中通过上下文意识的语义一致性推理缓解LLM幻觉雪球
IEEE transactions on neural networks and learning systems
|January 28, 2026
概括
大型语言模型 (LLM) 的协作放大了不真实的内容,导致幻觉变成了雪球. 本研究引入了一个框架,用于分析和减轻多剂系统中的这种效应.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 多代理系统 多代理系统
背景情况:
- 大型语言模型 (LLM) 实现了先进的多代理合作.
- 由LLM生成的不真实的内容导致合作中的幻觉雪球.
- 现有的研究缺乏在多剂环境中对幻觉的分析.
研究的目的:
- 提出一个框架来分析幻觉传播在连续的多剂合作.
- 开发验证和减轻幻觉雪球效应的方法.
- 在不改变模型架构的情况下解决幻觉传播问题.
主要方法:
- 使用概率模型的上下文感知幻觉分析框架.
- 标记级破坏序列检测用于效果验证.
- 使用双向卷入集群的语义推理授权的缓解.
主要成果:
- 在跨领域的多代理合作中验证了幻觉雪球的存在.
- 使用拟议的缓解策略,证明有效地减少幻觉传播.
- 从模型生成的内容和外部知识缺口中展示了减轻幻觉的效果.
结论:
- 多代理的LLM合作容易产生幻觉雪球.
- 拟议的框架有效地分析和减轻了这种影响.
- 语义推理提供了一种可行的方法来提高协作AI系统的可靠性.
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