Probing Materials Knowledge in LLMs: From Latent Embeddings to Reliable Predictions
Large language models are increasingly applied to materials science, yet fundamental questions remain about their reliability and knowledge encoding. Evaluating 25 LLMs across four materials science tasks -- over 200 base and fine-tuned configurations -- we find that output modality fundamentally determines model behavior. For symbolic tasks, fine-tuning converges to consistent, verifiable answers with reduced response entropy, while for numerical tasks, fine-tuning improves prediction accuracy but models remain inconsistent across repeated inference runs, limiting their reliability as quantitative predictors. For numerical regression, we find that better performance can be obtained by ex...
| Authors | Vineeth Venugopal, Soroush Mahjoubi, Elsa Olivetti |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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