Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus
Tianhang Zhang Lin Qiu Qipeng Guo Cheng Deng Yue Zhang Zheng Zhang Chenghu Zhou Xinbing Wang Luoyi Fu
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.