In-context learning has become an important approach for few-shot learning in Large Language Models because of its ability to rapidly adapt to new tasks without fine-tuning model parameters. However, it is restricted to applications in natural language and inapplicable to other domains. In this paper, we adapt the concepts underpinning in-context learning to develop a new algorithm for few-shot molecular property prediction. Our approach learns to predict molecular properties from a context of (molecule, property measurement) pairs and rapidly adapts to new properties without fine-tuning. On the FS-Mol and BACE molecular property prediction benchmarks, we find this method surpasses the performance of recent meta-learning algorithms at small support sizes and is competitive with the best methods at large support sizes.

翻译：上下文学习已成为大语言模型中少样本学习的重要方法，因其能在不调整模型参数的情况下快速适应新任务。然而，该方法仅限于自然语言领域，无法适用于其他领域。本文借鉴上下文学习的基本概念，提出了一种面向少样本分子性质预测的新算法。该方法通过（分子、性质测量值）对的上下文学习预测分子性质，并能无需微调即可快速适应新性质。在FS-Mol和BACE分子性质预测基准测试中，我们发现该方法在少量支持样本下超越了近期元学习算法的性能，并在大量支持样本下与最优方法具有竞争力。