幻觉能有所帮助吗？提升大语言模型在药物发现中的表现
Can Hallucinations Help? Boosting LLMs for Drug Discovery
AI总结：
本文研究LLM幻觉在药物发现中的价值，发现将基于SMILES生成的幻觉性文本纳入分子性质预测可显著提升部分模型准确率，其中结构误描述类幻觉影响最大，为利用幻觉作为科学建模中的有用信号提供了新思路。
摘要：
大语言模型（LLMs）中的幻觉——看似合理但事实不准确的文本——通常被视为不受欢迎的现象。然而，近期研究表明，这类输出可能具有创造性潜力。在本文中，我们探究幻觉能否提升LLMs在分子性质预测上的表现，这是早期药物发现中的一项关键任务。我们提示LLMs根据分子SMILES字符串生成自然语言描述，并将这些通常带有幻觉的描述纳入下游分类任务。在五个数据集上对七个经过指令微调的LLMs进行评估后，我们发现幻觉显著提高了部分模型的预测准确率。值得注意的是，当纳入幻觉文本时，Falcon3-Mamba-7B超越了所有基线模型，而GPT-4o生成的幻觉在不同模型之间始终带来最大增益。我们进一步识别并分类了超过18,000条有益幻觉，其中结构误描述成为影响最大的类型，这表明关于分子结构的幻觉性陈述可能提高模型置信度。消融研究表明，更大的模型从幻觉中获益更多，而温度的影响有限。我们的发现挑战了将幻觉视为纯粹问题的传统观点，并为在药物发现等科学建模任务中将幻觉作为有用信号加以利用指明了新方向。
英文摘要：
Hallucinations in large language models (LLMs), plausible but factually inaccurate text, are often viewed as undesirable. However, recent work suggests that such outputs may hold creative potential. In this paper, we investigate whether hallucinations can improve LLMs on molecule property prediction, a key task in early-stage drug discovery. We prompt LLMs to generate natural language descriptions from molecular SMILES strings and incorporate these often hallucinated descriptions into downstream classification tasks. Evaluating seven instruction-tuned LLMs across five datasets, we find that hallucinations significantly improve predictive accuracy for some models. Notably, Falcon3-Mamba-7B outperforms all baselines when hallucinated text is included, while hallucinations generated by GPT-4o consistently yield the greatest gains between models. We further identify and categorize over 18,000 beneficial hallucinations, with structural misdescriptions emerging as the most impactful type, suggesting that hallucinated statements about molecular structure may increase model confidence. Ablation studies show that larger models benefit more from hallucinations, while temperature has a limited effect. Our findings challenge conventional views of hallucination as purely problematic and suggest new directions for leveraging hallucinations as a useful signal in scientific modeling tasks like drug discovery.
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。
