Enhancing Hallucination Detection via Future Context
This addresses a critical challenge for users of online platforms where LLMs generate text without transparency, though it appears incremental as it builds on existing sampling-based methods.
The paper tackles the problem of detecting hallucinations in black-box large language model outputs by sampling future contexts, showing performance improvements across multiple detection methods.
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.