通过类比推理解锁大语言模型在科学中的创造力
Unlocking LLM Creativity in Science through Analogical Reasoning
AI总结：
本文通过类比推理方法提升大语言模型在开放性问题解决中的多样性与创新性，实验表明该方法显著提高了生成解决方案的质量和多样性。
摘要：
自主科学旨在增强科学发现，特别是在生物医学等复杂领域。然而，这需要AI系统能够持续生成新颖且多样的解决方案。我们评估了LLMs在开放性解决方案生成任务上的表现，并量化了其模式崩溃倾向。为缓解此问题，我们引入了类比推理（AR）作为新的解决方案生成方法。AR通过基于共享关系结构生成类比，然后利用这些类比寻找新颖的解决方案。与基线相比，AR发现的解决方案更加多样化（改进解决方案多样性指标90-173%），生成新颖解决方案的比例超过50%（相比基线的1.6%），并产生高质量的类比。为验证AR的现实可行性，我们在四个生物医学问题上实现了AR生成的解决方案，获得了一致的定量提升。AR生成的方法在扰动效应预测上实现了近13倍的改进，预测细胞间通讯时在AUPRC上优于所有基线，推断脑区相互作用时与已发表方法的Spearman相关性（ρ=0.729）较高，并在两个寡核苷酸性质预测数据集上实现了最先进性能。AR生成的创新和多样化的解决方案可用于增强现有解决方案生成方法的搜索空间。
英文摘要：
Autonomous science promises to augment scientific discovery, particularly in complex fields like biomedicine. However, this requires AI systems that can consistently generate novel and diverse solutions to open-ended problems. We evaluate LLMs on the task of open-ended solution generation and quantify their tendency to mode collapse into low-diversity generations. To mitigate this mode collapse, we introduce analogical reasoning (AR) as a new approach to solution generation. AR generates analogies to cross-domain problems based on shared relational structure, then uses those analogies to search for novel solutions. Compared to baselines, AR discovers significantly more diverse generations (improving solution diversity metrics by 90-173%), generates novel solutions over 50% of the time (compared to as little as 1.6% for baselines), and produces high-quality analogies. To validate the real-world feasibility of AR, we implement AR-generated solutions across four biomedical problems, yielding consistent quantitative gains. AR-generated approaches achieve a nearly 13-fold improvement on distributional metrics for perturbation effect prediction, outperform all baselines on AUPRC when predicting cell-cell communication, infer brain region interactions with a high Spearman correlation ($ρ$=0.729) to published methods, and establish state-of-the-art performance on 2 datasets for oligonucleotide property prediction. The novel and diverse solutions produced by AR can be used to augment the search space of existing solution generation methods.
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。
