Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Models

TL;DR

This paper presents AGSER, a novel attention-guided self-reflection method for zero-shot hallucination detection in large language models, improving accuracy and efficiency by leveraging attention contributions and consistency scoring.

Contribution

Introduces AGSER, a new zero-shot hallucination detection method that uses attention contributions and self-reflection to improve detection accuracy and reduce computational costs.

Findings

AGSER outperforms existing methods in zero-shot hallucination detection.

The approach requires only three passes through the LLMs.

AGSER demonstrates robustness across multiple benchmarks.

Abstract

Hallucination has emerged as a significant barrier to the effective application of Large Language Models (LLMs). In this work, we introduce a novel Attention-Guided SElf-Reflection (AGSER) approach for zero-shot hallucination detection in LLMs. The AGSER method utilizes attention contributions to categorize the input query into attentive and non-attentive queries. Each query is then processed separately through the LLMs, allowing us to compute consistency scores between the generated responses and the original answer. The difference between the two consistency scores serves as a hallucination estimator. In addition to its efficacy in detecting hallucinations, AGSER notably reduces computational overhead, requiring only three passes through the LLM and utilizing two sets of tokens. We have conducted extensive experiments with four widely-used LLMs across three different hallucination benchmarks, demonstrating that our approach significantly outperforms existing methods in zero-shot hallucination detection.