Enhancing Hallucination Detection via Future Context
Year: 2025
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Lee, Joosung, Park, Cheonbok, Jo, Hwiyeol, Kim, Jeonghoon, Park, Joonsuk, Yoo, Kang Min
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7077043129
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