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

2026-04-10

AI Summary

This study addresses longstanding criticisms that language models merely rely on surface-level statistical patterns in strings and thus fail to capture the essence of language. It identifies and refutes two prevalent misconceptions: the “string-statistics straw man” and the assumption that current model capabilities represent an inherent ceiling. By integrating perspectives from linguistics, cognitive science, neuroscience, philosophy, and computer science, the paper proposes a theoretical framework that transcends present limitations and systematically clarifies both the potential and boundaries of language models in linguistic research. This work not only establishes a new paradigm for the deep integration of artificial intelligence and language science but also lays a foundational theoretical groundwork for future interdisciplinary collaboration.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search

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.

Problem

• language models
• linguistic competence
• string statistics
• scientific understanding
• AI and language science

Innovation

• language models
• linguistic competence
• string statistics
• interdisciplinary research
• AI and language science