Language models (LMs) are currently at the forefront of NLP research due to their remarkable versatility across diverse tasks. However, a large gap exists between their observed capabilities and the explanations proposed by established formal machinery. To motivate a better theoretical characterization of LMs’ abilities and limitations, this tutorial aims to provide a comprehensive introduction to a specific framework for formal analysis of modern LMs using tools from formal language theory (FLT). We present how tools from FLT can be useful in understanding the inner workings and predicting the capabilities of modern neural LM architectures. We cover recent results using FLT to make precise and practically relevant statements about LMs based on recurrent neural networks and transformers by relating them to formal devices such as finite-state automata, Turing machines, and analog circuits. Altogether, the results covered in this tutorial allow us to make precise statements and explanations about the observed as well as predicted behaviors of LMs, as well as provide theoretically motivated suggestions on the aspects of the architectures that could be improved.

As transformers have gained prominence in natural language processing, some researchers have investigated theoretically what problems they can and cannot solve, by treating problems as formal languages. Exploring such questions can help clarify the power of transformers relative to other models of computation, their fundamental capabilities and limits, and the impact of architectural choices. Work in this subarea has made considerable progress in recent years. Here, we undertake a comprehensive survey of this work, documenting the diverse assumptions that underlie different results and providing a unified framework for harmonizing seemingly contradictory findings.