通过未来上下文增强幻觉检测
Enhancing Hallucination Detection via Future Context
AI总结：
本文提出通过采样未来上下文来增强幻觉检测，结合多种采样方法提升检测性能。
摘要：
大型语言模型（LLMs）被广泛用于生成在线平台上的合理文本，但不揭示生成过程。随着用户越来越多地遇到此类黑盒输出，检测幻觉成为关键挑战。为解决此问题，我们开发了针对黑盒生成器的幻觉检测框架。受幻觉一旦引入便持续存在的观察启发，我们采样未来上下文。这些采样得到的未来上下文为幻觉检测提供了有价值线索，并可有效整合各种基于采样的方法。我们通过多种方法广泛展示了所提出采样方法带来的性能提升。
英文摘要：
Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process. As users increasingly encounter such black-box outputs, detecting hallucinations has become a critical challenge. To address this challenge, we focus on developing a hallucination detection framework for black-box generators. Motivated by the observation that hallucinations, once introduced, tend to persist, we sample future contexts. The sampled future contexts provide valuable clues for hallucination detection and can be effectively integrated with various sampling-based methods. We extensively demonstrate performance improvements across multiple methods using our proposed sampling approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
