ChatIPC system extracts ordered token-transition rules from text for interpretable ML

This paper introduces Chat Incremental Pattern Constructor (ChatIPC), a system designed for interpretable machine learning. ChatIPC extracts ordered token-transition rules from text and enhances them with definition-based expansion. The system constructs responses by selecting candidates based on similarity, functioning as a rule extractor on a token graph rather than a traditional classifier. AI

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IMPACT Introduces a novel approach to rule extraction for enhanced interpretability in machine learning systems.

RANK_REASON This is a research paper detailing a new system for rule extraction in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• Chat Incremental Pattern Constructor
• ChatIPC
• Caleb Nwokocha
• arXiv

• paper
• other