Coding LLMs Can Be Harnessed to Learn Interpretable Models from Data

arXiv:2610.14006v1 Announce Type: cross Abstract: Since compact, interpretable classifiers correspond to simple programs, it is natural to ask if LLM-based coding agents can be used to learn interpretable models from data. In this paper we present a simple harness that repeatedly prompts a coding LLM to produce the next rule in a rule set, given a sample of labeled examples. We show that this approach is competitive with, and often superior to, state-of-the-art rule-learning methods on tabular data, especially with small training sets, and wins by large margins on text, including against another LLM-based rule learner; that this approach outperforms standard harnesses, and that the approach immediately leads to new kinds of interpretable models, since coding agents are not restricted to search a highly restricted hypothesis space. In addition to the advances made in learning interpretable models, these results are of interest because they introduce a new class of coding benchmarks - construction of interpretable classifiers - which includes quantitative measurements of output quality (i.e., test-set error) and existing mature ``specialist coding algorithms'' (i.e., existing learning methods).

Abstract:Since compact, interpretable classifiers correspond to simple programs, it is natural to ask if LLM-based coding agents can be used to learn interpretable models from data. In this paper we present a simple harness that repeatedly prompts a coding LLM to produce the next rule in a rule set, given a sample of labeled examples. We show that this approach is competitive with, and often superior to, state-of-the-art rule-learning methods on tabular data, especially with small training sets, and wins by large margins on text, including against another LLM-based rule learner; that this approach outperforms standard harnesses, and that the approach immediately leads to new kinds of interpretable models, since coding agents are not restricted to search a highly restricted hypothesis space. In addition to the advances made in learning interpretable models, these results are of interest because they introduce a new class of coding benchmarks - construction of interpretable classifiers - which includes quantitative measurements of output quality (i.e., test-set error) and existing mature ``specialist coding algorithms'' (i.e., existing learning methods).

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.14006 [cs.LG]

(or arXiv:2610.14006v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.14006 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Coding LLMs Can Be Harnessed to Learn Interpretable Models from Data

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