LLM knowledge for improved Out-of-Distribution Tabular Prediction - A Cross-Validated Regularisation Approach
Read the original on arXiv Statistics ML →The study investigates whether coefficient priors derived from large language models (LLMs) can enhance out‑of‑distribution (OOD) tabular prediction by serving as a shrinkage target in regularised regression. Across five datasets, LLM‑elicited priors improved OOD mean squared error on four datasets by up to 7.9% and yielded better probabilistic accuracy in classification tasks, though the gains varied and were sometimes comparable to in‑domain improvements. Cross‑validation was used to select priors, but the variability among candidates meant that the method mainly avoided the worst priors rather than consistently finding the best. "whyItMatters":"The findings suggest that LLM‑derived priors can provide a scalable alternative to expert elicitation for improving model robustness to distribution shifts, though their effectiveness depends on dataset size and prior variability."
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