arXiv:2607.01293v1 Announce Type: new 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,
The proliferation of powerful large language models necessitates better methods for controlling and grounding their outputs, making frameworks like RuleChef highly relevant.
This development allows for LLMs to generate transparent and human-editable rules, bridging the gap between powerful black-box models and accountable, explainable AI systems, which is crucial for enterprise adoption.
Previously opaque AI decision-making can now be augmented with auditable, human-interpretable rule sets, potentially improving trust and control in complex NLP applications.
- · Enterprises adopting AI
- · NLP developers
- · AI governance/ethics services
- · Rule-based system vendors
- · Vendors of purely black-box AI solutions
- · Organizations with high-risk, unexplainable AI deployments
Increased control and explainability for LLM-powered NLP applications become widely accessible.
This framework could enable 'human-in-the-loop' AI at a new level of granularity, allowing for more robust and responsible autonomous systems.
The ability to bootstrap rules from existing models suggests a path towards AI systems that can learn and codify institutional knowledge in an adaptable, transferrable format, potentially accelerating enterprise automation.
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Read at arXiv cs.CL