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This paper introduces LeDQeR, a framework that uses large language models to automatically generate data quality rules for enterprise tools, improving rule coverage and reducing manual maintenance effort.
RuleChef is a framework that uses LLMs to generate human-editable, executable rules for NLP tasks, iteratively improving them based on examples and human feedback, resulting in fast, deterministic, and inspectable rule systems.
This paper presents a multi-agent LLM pipeline that automatically generates and verifies reaction rules for chemical synthesis, expanding a standard taxonomy from 68 to 14,073 classes without human curation, achieving 97.7% classification accuracy on unseen reactions.