Abstract
RuleChef utilizes large language models to generate and iteratively improve executable rules for NLP tasks through example-based learning and human feedback, resulting in fast and inspectable rule systems.
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
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This paper has a personal motivation for me as well — I've been in the explainable AI space for more than 10 years now, and building rule systems has been close to my heart since then. With RuleChef we're trying to tackle the burden of actually building and maintaining rule systems for your task: the LLM writes and repairs the rules, and at inference only the rules run.
Very interested in your feedback :)
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