Do LLMs Know When They Are Wrong About Molecules? In-Context Property Prediction with Self-Reported Uncertainty

arXiv:2610.41745v1 Announce Type: new Abstract: Large language models (LLMs) are used in many domains, but molecular property prediction with them is still little explored. In this work we evaluate the capability of 7 frontier LLMs to predict molecular properties together with a self-reported uncertainty from in-context examples, and compare them with classical baselines trained on the same molecules. We use 4 datasets and screen each for contamination of the training data of the LLMs: three are clean, and the fourth, ESOL, is memorised by most of the models and serves as a comparison. On the clean datasets every LLM improves with the context, at 30 examples the most accurate one beats every baseline. The requested reasoning effort changes accuracy little. The self-reported uncertainty ranks the prediction errors weakly, with Spearman rank correlations of up to 0.46, about the level reached by the uncertainties of the baselines. Combining uncertainty and prediction,…