The Generation-Recognition Asymmetry: Six Dimensions of a Fundamental Divide in Formal Language Theory
A new paper published on arXiv explores the fundamental differences between language generation and recognition. It identifies six dimensions where these processes diverge, including computational complexity, ambiguity, and directionality. The research challenges the common notion that generation is always easier than recognition, highlighting that constrained generation can be NP-hard. The paper also introduces temporality as a previously unrecognized dimension of this asymmetry and connects it to surprisal in natural language processing. AI
IMPACT Introduces a theoretical framework that could influence future NLP model design and evaluation.