Robust Uncertainty Quantification for Factual Generation of Large Language Models

TL;DR

This paper introduces a novel uncertainty quantification method (RU) to improve the detection of hallucinations in large language models during factual generation, especially under adversarial questioning, enhancing reliability.

Contribution

The study proposes a robust uncertainty quantification approach tailored for factual generation, addressing limitations of existing methods in non-canonical and adversarial scenarios.

Findings

RU outperforms baseline methods in trap question tests

Average ROCAUC increase of 0.1-0.2 across four models

Effective in detecting hallucinations in factual generation

Abstract

The rapid advancement of large language model(LLM) technology has facilitated its integration into various domains of professional and daily life. However, the persistent challenge of LLM hallucination has emerged as a critical limitation, significantly compromising the reliability and trustworthiness of AI-generated content. This challenge has garnered significant attention within the scientific community, prompting extensive research efforts in hallucination detection and mitigation strategies. Current methodological frameworks reveal a critical limitation: traditional uncertainty quantification approaches demonstrate effectiveness primarily within conventional question-answering paradigms, yet exhibit notable deficiencies when confronted with non-canonical or adversarial questioning strategies. This performance gap raises substantial concerns regarding the dependability of LLM responses in real-world applications requiring robust critical thinking capabilities. This study aims to fill this gap by proposing an uncertainty quantification scenario in the task of generating with multiple facts. We have meticulously constructed a set of trap questions contained with fake names. Based on this scenario, we innovatively propose a novel and robust uncertainty quantification method(RU). A series of experiments have been conducted to verify its effectiveness. The results show that the constructed set of trap questions performs excellently. Moreover, when compared with the baseline methods on four different models, our proposed method has demonstrated great performance, with an average increase of 0.1-0.2 in ROCAUC values compared to the best performing baseline method, providing new sights and methods for addressing the hallucination issue of LLMs.