如何检测和击败分子幻象：基于LLM的分子理解中幻觉问题的度量驱动基准
How to Detect and Defeat Molecular Mirage: A Metric-Driven Benchmark for Hallucination in LLM-based Molecular Comprehension
AI总结：
针对LLMs在分子理解中的幻觉问题，本文分析其来源，提出Mol-Hallu评估指标量化幻觉程度，还设计HRPP后处理阶段缓解幻觉，实验验证其有效性，为提升LLMs科学应用可靠性提供关键见解。
摘要：
大型语言模型（LLMs）在科学领域的应用日益广泛，尤其用于分子理解与分析。然而，现有模型受幻觉问题影响，导致药物设计和使用中出现错误。本文首先分析了LLMs在分子理解任务中的幻觉来源，特别是PubChem数据集中观察到的知识捷径现象。为了以计算高效的方式评估分子理解任务中的幻觉，我们引入了\textbf{Mol-Hallu}——一种新颖的自由形式评估指标，它基于生成文本与实际分子特性之间的科学蕴含关系量化幻觉程度。利用Mol-Hallu指标，我们重新评估并分析了不同LLMs在执行分子理解任务时的幻觉程度。此外，我们提出了幻觉减少后处理阶段（HRPP）以缓解分子幻觉。实验表明HRPP在仅解码器和编码器-解码器分子LLMs上均有效。我们的研究结果为减轻幻觉、提高LLMs在科学应用中的可靠性提供了关键见解。
英文摘要：
Large language models are increasingly used in scientific domains, especially for molecular understanding and analysis. However, existing models are affected by hallucination issues, resulting in errors in drug design and utilization. In this paper, we first analyze the sources of hallucination in LLMs for molecular comprehension tasks, specifically the knowledge shortcut phenomenon observed in the PubChem dataset. To evaluate hallucination in molecular comprehension tasks with computational efficiency, we introduce \textbf{Mol-Hallu}, a novel free-form evaluation metric that quantifies the degree of hallucination based on the scientific entailment relationship between generated text and actual molecular properties. Utilizing the Mol-Hallu metric, we reassess and analyze the extent of hallucination in various LLMs performing molecular comprehension tasks. Furthermore, the Hallucination Reduction Post-processing stage~(HRPP) is proposed to alleviate molecular hallucinations, Experiments show the effectiveness of HRPP on decoder-only and encoder-decoder molecular LLMs. Our findings provide critical insights into mitigating hallucination and improving the reliability of LLMs in scientific applications.
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。
