Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus
This addresses the issue of costly and inefficient hallucination detection for users of LLMs in real-world applications, representing a novel method for a known bottleneck.
The paper tackles the problem of detecting hallucinations in Large Language Models by proposing a reference-free, uncertainty-based method that focuses on keywords, unreliable tokens, and token properties, achieving state-of-the-art performance across all evaluation metrics.
Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields. However, LLMs are prone to hallucinate untruthful or nonsensical outputs that fail to meet user expectations in many real-world applications. Existing works for detecting hallucinations in LLMs either rely on external knowledge for reference retrieval or require sampling multiple responses from the LLM for consistency verification, making these methods costly and inefficient. In this paper, we propose a novel reference-free, uncertainty-based method for detecting hallucinations in LLMs. Our approach imitates human focus in factuality checking from three aspects: 1) focus on the most informative and important keywords in the given text; 2) focus on the unreliable tokens in historical context which may lead to a cascade of hallucinations; and 3) focus on the token properties such as token type and token frequency. Experimental results on relevant datasets demonstrate the effectiveness of our proposed method, which achieves state-of-the-art performance across all the evaluation metrics and eliminates the need for additional information.