Characterizing the Expressivity of Transformer Language Models
Jiaoda Li, Ryan Cotterell
Transformer-based language models (LMs) have achieved widespread empirical success, but their theoretical expressive power remains only partially understood. Prior work often relies on idealized models with assumptions---such as arbitrary numerical precision and hard attention---that diverge from real-world transformers.  In this work, we provide an exact characterization of fixed-precision transformers with strict future masking and soft attention, an idealization that more closely mirrors practical implementations. We show that these models are precisely as expressive as a specific fragment of linear temporal logic that includes only a single temporal operator: the past operator. We further relate this logic to established classes in formal language theory, automata theory, and algebra, yielding a rich and unified theoretical framework for understanding transformer expressivity. Finally, we present empirical results that align closely with our theory: transformers trained on languages within their theoretical capacity generalize perfectly over lengths, while they consistently fail to generalize on languages beyond it.