AINeutralarXiv – CS AI · 7h ago6/10
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Investigating More Explainable and Partition-Free Compositionality Estimation for LLMs: A Rule-Generation Perspective
Researchers propose a novel rule-generation approach to evaluate compositionality in large language models, addressing critical limitations in existing assessment methods that lack explainability and suffer from dataset partition leakage. This new framework requires LLMs to generate executable programs as rules for data mapping, providing more robust insights into how well these models generalize compositional concepts.