Analyzing constrained LLM through PDFA-learning (2406.08269v2)

Abstract: We define a congruence that copes with null next-symbol probabilities that arise when the output of a LLM 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.