You Can't Fight in Here! This is BBS!

Generative AI & LLMs

Published: arXiv: 2604.09501v1

Authors

Richard Futrell Kyle Mahowald

Abstract

Norm, the formal theoretical linguist, and Claudette, the computational language scientist, have a lovely time discussing whether modern language models can inform important questions in the language sciences. Just as they are about to part ways until they meet again, 25 of their closest friends show up -- from linguistics, neuroscience, cognitive science, psychology, philosophy, and computer science. We use this discussion to highlight what we see as some common underlying issues: the String Statistics Strawman (the mistaken idea that LMs can't be linguistically competent or interesting because they, like their Markov model predecessors, are statistical models that learn from strings) and the As Good As it Gets Assumption (the idea that LM research as it stands in 2026 is the limit of what it can tell us about linguistics). We clarify the role of LM-based work for scientific insights into human language and advocate for a more expansive research program for the language sciences in the AI age, one that takes on the commentators' concerns in order to produce a better and more robust science of both human language and of LMs.

Paper Summary

Problem

The main problem this paper addresses is the misconception that language models (LMs) are limited in their ability to understand and generate human language due to their statistical nature. This misconception, known as the String Statistics Strawman, assumes that LMs can't be linguistically competent or interesting because they are trained on strings of text, like their predecessors. Additionally, the paper addresses the As Good As It Gets Assumption, which suggests that current LM research is the limit of what it can tell us about linguistics.

Key Innovation

The key innovation of this paper is its attempt to clarify the role of LMs in language science and advocate for a more expansive research program. The authors propose a middle ground position, arguing that LMs don't replace linguistic theories, but rather complement them. They also highlight the potential of LMs to learn internal systems that generate hierarchical thought structures, challenging the String Statistics Strawman.

Practical Impact

This research has practical implications for the development of more effective language models and the integration of linguistic theories with AI research. By challenging the limitations of current LM research, the authors aim to encourage a more collaborative and interdisciplinary approach to language science. This could lead to the creation of more sophisticated language models that can better understand and generate human language, with applications in areas such as natural language processing, machine translation, and human-computer interaction.

Analogy / Intuitive Explanation

Imagine a car that can drive on a highway, but is limited to a fixed route. The String Statistics Strawman is like assuming that this car can only drive on that fixed route, because it's a car and cars are limited to roads. However, the authors suggest that this car can actually learn to navigate through the city, using its GPS and mapping abilities to create a new route. Similarly, LMs can learn to generate hierarchical thought structures, challenging the assumption that they are limited to string-based models.

Paper Information

Categories:

cs.CL

Published Date:

arXiv ID:

2604.09501v1

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