학술논문
Analyzing constrained LLM through PDFA-learning
Document Type
Working Paper
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Abstract
We define a congruence that copes with null next-symbol probabilities that arise when the output of a language model is constrained by some means during text generation. We develop an algorithm for efficiently learning the quotient with respect to this congruence and evaluate it on case studies for analyzing statistical properties of LLM.
Comment: Workshop Paper
Comment: Workshop Paper