Tal Linzen
Associate Professor of Linguistics and Data Science
Studies language models and computational psycholinguistics, comparing how neural networks and humans learn syntax and generalize.
Tal Linzen's lab at NYU works on multilingual, translation & linguistics, large language models and data management & big data. 38 papers since 2023, 19 at top venues, mostly ACL and NAACL. Applications close .
At a glance
- papers, 2023–2026
- 10/ year
- at T1 venues
- 50%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 11 papers2026 · T1 6 · collecting
- 8 papers2025 · T1 5
- 10 papers2024 · T1 4
- 9 papers2023 · T1 4
T1 venuesOtherVenues: ACL, NAACL, EMNLP, NeurIPS, Findings of EMNLP
Research focuspaper titles that use each topic's words
- Large language models23 papers
- Data management & big data4 papers
- Multilingual, translation & linguistics1 paper
- psycholinguistics1 paper
- syntax1 paper
Paper list
From the lab homepage.
202611 papers
- NeurIPS (T1)Always Learning, Always Mixing: Efficient and Simple Data Mixing All The Time (opens in a new tab)
- Nature Communications (T1)Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models (opens in a new tab)
- COLMBeyond the Rosetta Stone: Unification Forces in Generalization Dynamics (opens in a new tab)
- COLMCan LLMs Introspect? A Reality Check (opens in a new tab)
- Cognitive Science SocietyDeconstructing sentence disambiguation by joint latent modeling of reading paradigms: LLM surprisal is not enough (opens in a new tab)
- PNAS (T1)Eye movements reveal a dissociation between prediction and structural processing difficulty in language comprehension (opens in a new tab)
- ACL (T1)Language Models Struggle to Use Representations Learned In-Context (opens in a new tab)
- Journal of Memory and LanguageManipulating language models' training data to study syntactic constraint learning: the case of English passivization (opens in a new tab)
- TACL (T1)RELIC: Evaluating Complex Reasoning via the Recognition of Languages In-Context (opens in a new tab)
- NeurIPS (T1)Simulating Human Memory with Language Models (opens in a new tab)
- Trends in Cognitive SciencesTo model human linguistic prediction, make LLMs less superhuman (opens in a new tab)
20258 papers
- ACL (T1)Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases (opens in a new tab)
- Journal of Memory and LanguageBigger is not always better: The importance of human-scale language modeling for psycholinguistics (opens in a new tab)
- NeurIPS (T1)Emergence of Linear Truth Encodings in Language Models (opens in a new tab)
- Transactions of Machine Learning ResearchHow Does Code Pretraining Affect Language Model Task Performance? (opens in a new tab)
- Journal of Memory and LanguageLearning Filler-Gap Dependencies with Neural Language Models: Testing Island Sensitivity in Norwegian and English (opens in a new tab)
- EMNLP (T1)Multilingual Prompting for Improving LLM Generation Diversity (opens in a new tab)
- EMNLP (T1)Rapid Word Learning Through Meta In-Context Learning (opens in a new tab)
- NAACL (T1)What Goes Into a LM Acceptability Judgment? Rethinking the Impact of Frequency and Length (opens in a new tab)
202410 papers
- NAACL (T1)A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models (opens in a new tab)
- NeurIPS System 2 Reasoning At Scale WorkshopCan Language Models Perform Implicit Bayesian Inference Over User Preference States? (opens in a new tab)
- Findings of ACLCan You Learn Semantics Through Next-Word Prediction? The Case of Entailment (opens in a new tab)
- Computational Linguistics (T1)Do language models' words refer? (opens in a new tab)
- The BabyLM Challenge at CoNLLFindings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora (opens in a new tab)
- NAACL (T1)In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax (opens in a new tab)
- Journal of Memory and LanguageLarge-scale benchmark yields no evidence that language model surprisal explains syntactic disambiguation difficulty (opens in a new tab)
- Open MindNeural networks as cognitive models of the processing of syntactic constraints (opens in a new tab)
- CoNLLSPAWNing Structural Priming Predictions from a Cognitively Motivated Parser (opens in a new tab)
- NAACL (T1)The Impact of Depth on Compositional Generalization in Transformer Language Models (opens in a new tab)
20239 papers
- Findings of EMNLPA Language Model with Limited Memory Capacity Captures Interference in Human Sentence Processing (opens in a new tab)
- Proceedings of the BabyLM ChallengeFindings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora (opens in a new tab)
- TACL (T1)How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN (opens in a new tab)
- ACL (T1)How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech (opens in a new tab)
- ACL (T1)How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases (opens in a new tab)
- Society for Computation in LinguisticsLanguage models can learn exceptions to syntactic rules (opens in a new tab)
- Society for Computation in LinguisticsNeural Networks Can Learn Patterns of Island-insensitivity in Norwegian (opens in a new tab)
- EMNLP (T1)SLOG: A Structural Generalization Benchmark for Semantic Parsing (opens in a new tab)
- Findings of EMNLPVerb Conjugation in Transformers Is Determined by Linear Encodings of Subject Number (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to NYUFall 2027
Computer Science PhD (Courant / Tandon)
- Round
- Single round (December)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
Similar labsnearest by research summary
- CNTsinghua
Hua Xu
- Multimodal & grounded language
- SGNTU
Wei Lu
- Large language models
- USMichigan
Rada Mihalcea
- Language & society
- USGeorgia Tech
Wei Xu
- Knowledge, QA & retrieval
- CAToronto
Colin Raffel
- Large language models
- CNPeking
Yansong Feng
- Knowledge, QA & retrieval
Your notes
Save this lab to keep notes.
Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Not collected
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.
NYUcloses