Statsformer: Validated Ensemble Learning with LLM-Derived Semantic Priors

• URL: http://arxiv.org/abs/2601.21410v2
• Date: Wed, 04 Feb 2026 21:58:51 GMT
• Title: Statsformer: Validated Ensemble Learning with LLM-Derived Semantic Priors
• Authors: Erica Zhang, Naomi Sagan, Danny Tse, Fangzhao Zhang, Mert Pilanci, Jose Blanchet,
• Abstract summary: Statsformer is a principled framework for integrating large language model (LLM)-derived knowledge into supervised statistical learning.<n>We embed LLM-derived feature priors within an ensemble of linear and nonlinear learners, adaptively calibrating their influence via cross-validation.<n>This design yields a flexible system with an oracle-style guarantee that it performs no worse than any convex combination of its in-library base learners, up to statistical error.
• Score: 43.18750992853517
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Statsformer, a principled framework for integrating large language model (LLM)-derived knowledge into supervised statistical learning. Existing approaches are limited in adaptability and scope: they either inject LLM guidance as an unvalidated heuristic, which is sensitive to LLM hallucination, or embed semantic information within a single fixed learner. Statsformer overcomes both limitations through a guardrailed ensemble architecture. We embed LLM-derived feature priors within an ensemble of linear and nonlinear learners, adaptively calibrating their influence via cross-validation. This design yields a flexible system with an oracle-style guarantee that it performs no worse than any convex combination of its in-library base learners, up to statistical error. Empirically, informative priors yield consistent performance improvements, while uninformative or misspecified LLM guidance is automatically downweighted, mitigating the impact of hallucinations across a diverse range of prediction tasks.An open-source implementation of Statsformer is available at https://github.com/pilancilab/statsformer.

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