Enhancing Hallucination Detection via Future Context

TL;DR

This paper proposes a novel hallucination detection framework for black-box language models by sampling future contexts, which improves detection performance across multiple methods.

Contribution

It introduces a sampling-based approach leveraging future contexts to enhance hallucination detection in black-box language models.

Findings

Performance improved across multiple detection methods.

Future context sampling provides valuable clues for hallucination detection.

Sampling approach effectively integrates with existing methods.

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.