HuDEx：整合幻觉检测与可解释性以增强LLM响应的可靠性
HuDEx: Integrating Hallucination Detection and Explainability for Enhancing the Reliability of LLM responses
AI总结：
针对LLM幻觉损害可靠性问题，提出整合幻觉检测与解释的模型HuDEx，其检测精度超越Llama3 70B和GPT-4，零样本表现良好，通过解释增强模型可靠性，推动幻觉检测研究。
摘要：
大型语言模型（LLMs）的最新进展展现出良好的改进，在自然语言处理的各类下游任务中常超越现有方法。但这些模型仍面临挑战，可能阻碍其实用性。例如，幻觉现象会损害LLMs的可靠性，尤其在要求高事实精度的领域。当前基准主要聚焦幻觉检测与事实性评估，但未超出识别范畴。本文提出一种增强解释的幻觉检测模型HuDEx，旨在通过检测幻觉和提供详细解释来提升LLM生成响应的可靠性。该模型提供了整合检测与解释的新方法，让用户和LLM自身都能理解并减少错误。实验结果显示，该模型在幻觉检测精度上超越Llama3 70B和GPT-4等更大规模LLMs，同时保持可靠解释；且在零样本及其他测试环境中表现良好，展现出跨不同基准数据集的适应性。该方法通过引入整合可解释性与幻觉检测的新途径，进一步推动了幻觉检测研究，提升了语言模型幻觉评估的性能和可靠性。
英文摘要：
Recent advances in large language models (LLMs) have shown promising improvements, often surpassing existing methods across a wide range of downstream tasks in natural language processing. However, these models still face challenges, which may hinder their practical applicability. For example, the phenomenon of hallucination is known to compromise the reliability of LLMs, especially in fields that demand high factual precision. Current benchmarks primarily focus on hallucination detection and factuality evaluation but do not extend beyond identification. This paper proposes an explanation enhanced hallucination-detection model, coined as HuDEx, aimed at enhancing the reliability of LLM-generated responses by both detecting hallucinations and providing detailed explanations. The proposed model provides a novel approach to integrate detection with explanations, and enable both users and the LLM itself to understand and reduce errors. Our measurement results demonstrate that the proposed model surpasses larger LLMs, such as Llama3 70B and GPT-4, in hallucination detection accuracy, while maintaining reliable explanations. Furthermore, the proposed model performs well in both zero-shot and other test environments, showcasing its adaptability across diverse benchmark datasets. The proposed approach further enhances the hallucination detection research by introducing a novel approach to integrating interpretability with hallucination detection, which further enhances the performance and reliability of evaluating hallucinations in language models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
