Tal Linzen

About

Tal Linzen is a researcher whose work focuses on understanding the linguistic capabilities of neural language models and their relation to human language processing. His research explores how models learn syntactic constraints, semantic representations, and generalization patterns, often through the manipulation of training data and the analysis of in-context learning. Linzen's contributions include investigating the emergence of linguistic biases in large language models, evaluating their reasoning abilities, and examining their capacity for compositional generalization and semantic understanding. His work also involves developing benchmarks and methodologies to assess the linguistic and cognitive properties of language models, with an emphasis on how these models compare to human language processing. Linzen's research spans multiple aspects of computational linguistics, psycholinguistics, and artificial intelligence, aiming to bridge the gap between machine learning models and human linguistic behavior.

Research topics

• Artificial Intelligence
• Computer Science
• Natural Language Processing
• Mathematics
• Psychology
• Programming language
• Statistics
• Data science
• Linguistics

Selected publications

• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

  arXiv (Cornell University) · 2022 · 548 citations

  Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyon…

  DOI

• COGS: A Compositional Generalization Challenge Based on Semantic Interpretation

  2020 · 149 citations

  Senior authorCorresponding

  Natural language is characterized by compositionality: the meaning of a complex expression is constructed from the meanings of its constituent parts. To facilitate the evaluation of the compositional abilities of language processing architectures, we introduce COGS, a semantic parsing dataset based on a fragment of English. The evaluation portion of COGS contains multiple systematic gaps that can only be addressed by compositional generalization; these include new combinations of familiar syntac…

  DOI

• Single‐Stage Prediction Models Do Not Explain the Magnitude of Syntactic Disambiguation Difficulty

  Cognitive Science · 2021 · 64 citations

  Senior authorCorresponding

  The disambiguation of a syntactically ambiguous sentence in favor of a less preferred parse can lead to slower reading at the disambiguation point. This phenomenon, referred to as a garden-path effect, has motivated models in which readers initially maintain only a subset of the possible parses of the sentence, and subsequently require time-consuming reanalysis to reconstruct a discarded parse. A more recent proposal argues that the garden-path effect can be reduced to surprisal arising in a ful…

  PublisherOA PDFDOI

• Characterizing Verbatim Short-Term Memory in Neural Language Models

  2022 · 3 citations

  Senior authorCorresponding

  When a language model is trained to predict natural language sequences, its prediction at each moment depends on a representation of prior context. What kind of information about the prior context can language models retrieve? We tested whether language models could retrieve the exact words that occurred previously in a text. In our paradigm, language models (transformers and an LSTM) processed English text in which a list of nouns occurred twice. We operationalized retrieval as the reduction in…

  DOI

• Learning filler-gap dependencies with neural language models: Testing island sensitivity in Norwegian and English

  Journal of Memory and Language · 2025-07-20 · 1 citations

  articleOpen access

  Human linguistic input is often claimed to be impoverished with respect to linguistic evidence for complex structural generalizations that children induce. The field of language acquisition is currently debating the ability of various learning algorithms to accurately derive target generalizations from the input. A growing body of research explores whether Neural Language Models (NLMs) can induce human-like generalizations about filler-gap dependencies (FGDs) in English, including island constra…

  PublisherDOI

Recent grants

• Collaborative Research: Inductive Biases for the Acquisition of Syntactic Transformations in Neural Networks

  NSF · $289k · 2020–2025

• CompCog: Collaborative Research: Testing quantitative predictions of sentence processing theories with a large-scale eye-tracking database

  NSF · $283k · 2020–2025

• Collaborative Research: Inductive Biases for the Acquisition of Syntactic Transformations in Neural Networks

  NSF · $381k · 2019–2021

Frequent coauthors

• Aaron Mueller

  52 shared

• Najoung Kim

  39 shared

• Ellie Pavlick

  36 shared

• Samuel R. Bowman

  33 shared

• R. Thomas McCoy

  Princeton University

  32 shared

• Ian Tenney

  28 shared

• Stuart M. Shieber

  28 shared

• Sebastian Gehrmann

  28 shared

Labs

• Computation and Psycholinguistics LabPI

  What are the mental representations that constitute our knowledge of language? How do we use them to understand and produce language? How can we create computational systems that learn language as efficiently and robustly as humans?

Similar researchers at New York University

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• Eero Simoncellih-113
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