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 widelyused LLMs across three different hallucination benchmarks, demonstrating that our approach significantly outperforms existing methods in zero-shot hallucination detection.

Junfei Wu, Yue Ding, Guofan Liu, Tianze Xia, Ziyue Huang, Dianbo Sui, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.