40 citations · 100 across the 20 of their papers we have counts for
29 papers
Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear
R. Thomas McCoy
Futrell and Mahowald (2025) frame the success of neural language models (LMs) as supporting gradient, usage-based linguistic theories. I argue that LMs can also instantiate theorie…
You Can't Fight in Here! This is BBS!
Richard Futrell, Kyle Mahowald
Norm, the formal theoretical linguist, and Claudette, the computational language scientist, have a lovely time discussing whether modern language models can inform important questi…
Across the Levels of Analysis: Explaining Predictive Processing in Humans Requires More Than Machine-Estimated Probabilities
Sathvik Nair, Colin Phillips
Under the lens of Marr's levels of analysis, we critique and extend two claims about language models (LMs) and language processing: first, that predicting upcoming linguistic infor…
Are Language Models Models?
Philip Resnik
Futrell and Mahowald claim LMs "serve as model systems", but an assessment at each of Marr's three levels suggests the claim is clearly not true at the implementation level, poorly…
Linguists should learn to love speech-based deep learning models
Marianne de Heer Kloots, Paul Boersma, Willem Zuidema
Futrell and Mahowald present a useful framework bridging technology-oriented deep learning systems and explanation-oriented linguistic theories. Unfortunately, the target article's…
Large language models are not about natural language
Johan J. Bolhuis, Andrea Moro, Stephen Crain +1
Large Language Models are useless for linguistics, as they are probabilistic models that require a vast amount of data to analyse externalized strings of words. In contrast, human…
Language models as tools for investigating the distinction between possible and impossible natural languages
Julie Kallini, Christopher Potts
We argue that language models (LMs) have strong potential as investigative tools for probing the distinction between possible and impossible natural languages and thus uncovering t…
How Linguistics Learned to Stop Worrying and Love the Language Models
Richard Futrell, Kyle Mahowald
Language models can produce fluent, grammatical text. Nonetheless, some maintain that language models don't really learn language and also that, even if they did, that would not be…
Mission: Impossible Language Models
Julie Kallini, Isabel Papadimitriou, Richard Futrell +2
Chomsky and others have very directly claimed that large language models (LLMs) are equally capable of learning languages that are possible and impossible for humans to learn. Howe…
A Cross-Linguistic Pressure for Uniform Information Density in Word Order
Thomas Hikaru Clark, Clara Meister, Tiago Pimentel +4
While natural languages differ widely in both canonical word order and word order flexibility, their word orders still follow shared cross-linguistic statistical patterns, often at…
Analogy in Contact: Modeling Maltese Plural Inflection
Sara Court, Andrea D. Sims, Micha Elsner
Maltese is often described as having a hybrid morphological system resulting from extensive contact between Semitic and Romance language varieties. Such a designation reflects an e…
A unified information-theoretic model of EEG signatures of human language processing
Jiaxuan Li, Richard Futrell
We advance an information-theoretic model of human language processing in the brain, in which incoming linguistic input is processed at two levels, in terms of a heuristic interpre…
When classifying grammatical role, BERT doesn't care about word order... except when it matters
Isabel Papadimitriou, Richard Futrell, Kyle Mahowald
Because meaning can often be inferred from lexical semantics alone, word order is often a redundant cue in natural language. For example, the words chopped, chef, and onion are mor…
Grammatical cues to subjecthood are redundant in a majority of simple clauses across languages
Kyle Mahowald, Evgeniia Diachek, Edward Gibson +2
Grammatical cues are sometimes redundant with word meanings in natural language. For instance, English word order rules constrain the word order of a sentence like "The dog chewed…
Sensitivity as a Complexity Measure for Sequence Classification Tasks
Michael Hahn, Dan Jurafsky, Richard Futrell
We introduce a theoretical framework for understanding and predicting the complexity of sequence classification tasks, using a novel extension of the theory of Boolean function sen…
Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERT
Isabel Papadimitriou, Ethan A. Chi, Richard Futrell +1
We investigate how Multilingual BERT (mBERT) encodes grammar by examining how the high-order grammatical feature of morphosyntactic alignment (how different languages define what c…
Predicting cross-linguistic adjective order with information gain
William Dyer, Richard Futrell, Zoey Liu +1
Languages vary in their placement of multiple adjectives before, after, or surrounding the noun, but they typically exhibit strong intra-language tendencies on the relative order o…
Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models
Ethan Wilcox, Peng Qian, Richard Futrell +3
Humans can learn structural properties about a word from minimal experience, and deploy their learned syntactic representations uniformly in different grammatical contexts. We asse…
An information-theoretic account of semantic interference in word production
Richard Futrell
I present a computational-level model of semantic interference effects in word production. Word production is cast as a rate-distortion problem where an agent selects words to mini…
Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations
Ethan Wilcox, Roger Levy, Richard Futrell
Deep learning sequence models have led to a marked increase in performance for a range of Natural Language Processing tasks, but it remains an open question whether they are able t…
What Syntactic Structures block Dependencies in RNN Language Models?
Ethan Wilcox, Roger Levy, Richard Futrell
Recurrent Neural Networks (RNNs) trained on a language modeling task have been shown to acquire a number of non-local grammatical dependencies with some success. Here, we provide n…
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
Ethan Wilcox, Peng Qian, Richard Futrell +2
State-of-the-art LSTM language models trained on large corpora learn sequential contingencies in impressive detail and have been shown to acquire a number of non-local grammatical…
Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State
Richard Futrell, Ethan Wilcox, Takashi Morita +3
We deploy the methods of controlled psycholinguistic experimentation to shed light on the extent to which the behavior of neural network language models reflects incremental repres…
Do RNNs learn human-like abstract word order preferences?
Richard Futrell, Roger P. Levy
RNN language models have achieved state-of-the-art results on various tasks, but what exactly they are representing about syntax is as yet unclear. Here we investigate whether RNN…