Training Neural Networks as Recognizers of Formal Languages

TL;DR

This paper investigates the formal language recognition capabilities of neural networks by training them directly as classifiers on various languages, providing a new benchmark and insights into their reasoning power across the Chomsky hierarchy.

Contribution

It introduces a method for training neural networks as recognizers of formal languages and provides a benchmark dataset, FLaRe, for empirical evaluation of language recognition capabilities.

Findings

RNNs and LSTMs often outperform transformers in language recognition tasks.

Auxiliary objectives like language modeling can improve recognition performance.

Performance varies across architectures and languages, with no single approach being universally best.

Abstract

Characterizing the computational power of neural network architectures in terms of formal language theory remains a crucial line of research, as it describes lower and upper bounds on the reasoning capabilities of modern AI. However, when empirically testing these bounds, existing work often leaves a discrepancy between experiments and the formal claims they are meant to support. The problem is that formal language theory pertains specifically to recognizers: machines that receive a string as input and classify whether it belongs to a language. On the other hand, it is common instead to evaluate language models on proxy tasks, e.g., language modeling or sequence-to-sequence transduction, that are similar in only an informal sense to the underlying theory. We correct this mismatch by training and evaluating neural networks directly as binary classifiers of strings, using a general method that can be applied to a wide variety of languages. As part of this, we extend an algorithm recently proposed by Sn{\ae}bjarnarson et al. (2025) for efficient length-controlled sampling of strings from regular languages. We provide results on a variety of languages across the Chomsky hierarchy for three neural architectures: a simple RNN, an LSTM, and a causally-masked transformer. We find that the RNN and LSTM often outperform the transformer, and that auxiliary training objectives such as language modeling can help, although no single objective uniformly improves performance across languages and architectures. Our contributions will facilitate theoretically sound empirical testing of language recognition claims in future work. We have released our datasets as a benchmark called FLaRe (Formal Language Recognition), along with our code.