RuleChef: Grounding LLM Task Knowledge in Human-Editable Rules
RuleChef is a framework that uses LLMs to generate executable rules for NLP tasks such as text classification, NER, and relation extraction. Rules are generated from task descriptions and labeled examples, then iteratively improved via additional examples and human feedback. LLMs are used only at learning time, resulting in a fast, deterministic, and inspectable rule system. Preliminary evaluation on classification and NER tasks shows promise, and the system is open-sourced under Apache 2.0.
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[Submitted on 1 Jul 2026]
Title:RuleChef: Grounding LLM Task Knowledge in Human-Editable Rules
View a PDF of the paper titled RuleChef: Grounding LLM Task Knowledge in Human-Editable Rules, by \'Ad\'am Kov\'acs and 2 other authors
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Abstract:We present RuleChef, a framework that uses large language models (LLMs) to generate executable rules for NLP tasks such as text classification, Named Entity Recognition (NER), or relation extraction. Rules are generated based on a task description and a set of labeled examples, then they are iteratively improved based both on additional examples and on human feedback overexisting rules. RuleChef can also be used to bootstrap rules using the observed input-output pairs from any existing model for a given task. LLMs are used only at learning time, synthesizing rules and iteratively patching them based on failures measured on a held-out split. The result of this process is a fast, deterministic, and inspectable rule system. Preliminary evaluation is performed on both classification and NER tasks. We release RuleChef as open-source software under an Apache 2.0
Comments: 8 pages
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2607.01293 [cs.CL]
(or arXiv:2607.01293v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.01293
arXiv-issued DOI via DataCite
Submission history
From: Ádám Kovács [view email] [v1] Wed, 1 Jul 2026 13:09:49 UTC (46 KB)
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