Investigating the role of entropy in sentence processing. - Tal Linzen; T. Florian Jaeger (CMCL@ACL, 2014)

The timecourse of phonotactic learning. - Tal Linzen; Gillian Gallagher (CogSci, 2014)

A model of rapid phonotactic generalization. - Tal Linzen; Timothy J. O'Donnell (EMNLP, 2015)

Lexical Preactivation in Basic Linguistic Phrases. - Joseph Fruchter; Tal Linzen; Masha Westerlund; Alec Marantz (J. Cogn. Neurosci., 2015)

Evaluating vector space models using human semantic priming results. - Allyson Ettinger; Tal Linzen (RepEval@ACL, 2016)

Issues in evaluating semantic spaces using word analogies. - Tal Linzen (RepEval@ACL, 2016)

Quantificational features in distributional word representations. - Tal Linzen; Emmanuel Dupoux; Benjamin Spector (*SEM@ACL, 2016)

Uncertainty and Expectation in Sentence Processing: Evidence From Subcategorization Distributions. - Tal Linzen; T. Florian Jaeger (Cogn. Sci., 2016)

Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies. - Tal Linzen; Emmanuel Dupoux; Yoav Goldberg (Trans. Assoc. Comput. Linguistics, 2016)

Prediction and uncertainty in an artificial language. - Tal Linzen; Noam Siegelman; Louisa Bogaerts (CogSci, 2017)

Exploring the Syntactic Abilities of RNNs with Multi-task Learning. - Émile Enguehard; Yoav Goldberg; Tal Linzen (CoNLL, 2017)

Comparing Character-level Neural Language Models Using a Lexical Decision Task. - Gaël Le Godais; Tal Linzen; Emmanuel Dupoux (EACL (2), 2017)

Phonological (un)certainty weights lexical activation. - Laura Gwilliams; David Poeppel; Alec Marantz; Tal Linzen (CMCL, 2018)

Distinct patterns of syntactic agreement errors in recurrent networks and humans. - Tal Linzen; Brian Leonard (CogSci, 2018)

Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks. - Richard Thomas McCoy; Robert Frank 001; Tal Linzen (CogSci, 2018)

Modeling garden path effects without explicit hierarchical syntax. - Marten van Schijndel; Tal Linzen (CogSci, 2018)

Targeted Syntactic Evaluation of Language Models. - Rebecca Marvin; Tal Linzen (EMNLP, 2018)

A Neural Model of Adaptation in Reading. - Marten van Schijndel; Tal Linzen (EMNLP, 2018)

Colorless Green Recurrent Networks Dream Hierarchically. - Kristina Gulordava; Piotr Bojanowski; Edouard Grave; Tal Linzen; Marco Baroni (NAACL-HLT, 2018)

What can linguistics and deep learning contribute to each other? - Tal Linzen (CoRR, 2018)

Can Entropy Explain Successor Surprisal Effects in Reading? - Marten van Schijndel; Tal Linzen (CoRR, 2018)

Non-entailed subsequences as a challenge for natural language inference. - R. Thomas McCoy; Tal Linzen (CoRR, 2018)

Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference. - Tom McCoy 001; Ellie Pavlick; Tal Linzen (ACL (1), 2019)

Human few-shot learning of compositional instructions. - Brenden M. Lake; Tal Linzen; Marco Baroni (CogSci, 2019)

How much harder are hard garden-path sentences than easy ones? - Grusha Prasad; Tal Linzen (CogSci, 2019)

Using Priming to Uncover the Organization of Syntactic Representations in Neural Language Models. - Grusha Prasad; Marten van Schijndel; Tal Linzen (CoNLL, 2019)

Quantity doesn't buy quality syntax with neural language models. - Marten van Schijndel; Aaron Mueller; Tal Linzen (EMNLP/IJCNLP (1), 2019)

RNNs implicitly implement tensor-product representations. - R. Thomas McCoy; Tal Linzen; Ewan Dunbar; Paul Smolensky (ICLR (Poster), 2019)

Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages. - Shauli Ravfogel; Yoav Goldberg; Tal Linzen (NAACL-HLT (1), 2019)

Probing What Different NLP Tasks Teach Machines about Function Word Comprehension. - Najoung Kim; Roma Patel; Adam Poliak; Patrick Xia 002; Alex Wang; Tom McCoy 001; Ian Tenney; Alexis Ross; Tal Linzen; Benjamin Van Durme; Samuel R. Bowman; Ellie Pavlick (*SEM@NAACL-HLT, 2019)

Analyzing and interpreting neural networks for NLP: A report on the first BlackboxNLP workshop. - Afra Alishahi; Grzegorz Chrupala; Tal Linzen (Nat. Lang. Eng., 2019)

Representations of Syntax [MASK] Useful: Effects of Constituency and Dependency Structure in Recursive LSTMs. - Michael A. Lepori; Tal Linzen; R. Thomas McCoy (ACL, 2020)

How Can We Accelerate Progress Towards Human-like Linguistic Generalization? - Tal Linzen (ACL, 2020)

Syntactic Data Augmentation Increases Robustness to Inference Heuristics. - Junghyun Min; R. Thomas McCoy; Dipanjan Das 001; Emily Pitler; Tal Linzen (ACL, 2020)

Cross-Linguistic Syntactic Evaluation of Word Prediction Models. - Aaron Mueller; Garrett Nicolai; Panayiota Petrou-Zeniou; Natalia Talmina; Tal Linzen (ACL, 2020)

BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance. - R. Thomas McCoy; Junghyun Min; Tal Linzen (BlackboxNLP@EMNLP, 2020)

Discovering the Compositional Structure of Vector Representations with Role Learning Networks. - Paul Soulos; R. Thomas McCoy; Tal Linzen; Paul Smolensky (BlackboxNLP@EMNLP, 2020)

Neural Language Models Capture Some, But Not All Agreement Attraction Effects. - Suhas Arehalli; Tal Linzen (CogSci, 2020)

Universal linguistic inductive biases via meta-learning. - Richard Thomas McCoy; Erin Grant; Paul Smolensky; Tom Griffiths 001; Tal Linzen (CogSci, 2020)

COGS: A Compositional Generalization Challenge Based on Semantic Interpretation. - Najoung Kim; Tal Linzen (EMNLP (1), 2020)

Syntactic Structure from Deep Learning. - Tal Linzen; Marco Baroni (CoRR, 2020)

Does Syntax Need to Grow on Trees? Sources of Hierarchical Inductive Bias in Sequence-to-Sequence Networks. - R. Thomas McCoy; Robert Frank 001; Tal Linzen (Trans. Assoc. Comput. Linguistics, 2020)

Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models. - Matthew Finlayson; Aaron Mueller; Sebastian Gehrmann; Stuart M. Shieber; Tal Linzen; Yonatan Belinkov (ACL/IJCNLP (1), 2021)

The Language Model Understood the Prompt was Ambiguous: Probing Syntactic Uncertainty Through Generation. - Laura Aina; Tal Linzen (BlackboxNLP@EMNLP, 2021)

NOPE: A Corpus of Naturally-Occurring Presuppositions in English. - Alicia Parrish; Sebastian Schuster; Alex Warstadt; Omar Agha; Soo-Hwan Lee; Zhuoye Zhao; Samuel R. Bowman; Tal Linzen (CoNLL, 2021)

Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction. - Shauli Ravfogel; Grusha Prasad; Tal Linzen; Yoav Goldberg (CoNLL, 2021)

Does Putting a Linguist in the Loop Improve NLU Data Collection? - Alicia Parrish; William Huang; Omar Agha; Soo-Hwan Lee; Nikita Nangia; Alex Warstadt; Karmanya Aggarwal; Emily Allaway; Tal Linzen; Samuel R. Bowman (EMNLP (Findings), 2021)

Frequency Effects on Syntactic Rule Learning in Transformers. - Jason Wei; Dan Garrette; Tal Linzen; Ellie Pavlick (EMNLP (1), 2021)

Predicting Inductive Biases of Pre-Trained Models. - Charles Lovering; Rohan Jha; Tal Linzen; Ellie Pavlick (ICLR, 2021)

Single-Stage Prediction Models Do Not Explain the Magnitude of Syntactic Disambiguation Difficulty. - Marten van Schijndel; Tal Linzen (Cogn. Sci., 2021)

Evaluating Groundedness in Dialogue Systems: The BEGIN Benchmark. - Nouha Dziri; Hannah Rashkin; Tal Linzen; David Reitter (CoRR, 2021)

Learning to Generalize Compositionally by Transferring Across Semantic Parsing Tasks. - Wang Zhu 001; Peter Shaw 004; Tal Linzen; Fei Sha (CoRR, 2021)

Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models. - Aaron Mueller; Robert Frank 001; Tal Linzen; Luheng Wang; Sebastian Schuster (ACL (Findings), 2022)

LSTMs Can Learn Basic Wh- and Relative Clause Dependencies in Norwegian. - Anastasia Kobzeva; Suhas Arehalli; Tal Linzen; Dave Kush (CogSci, 2022)

Syntactic Surprisal From Neural Models Predicts, But Underestimates, Human Processing Difficulty From Syntactic Ambiguities. - Suhas Arehalli; Brian Dillon; Tal Linzen (CoNLL, 2022)

Characterizing Verbatim Short-Term Memory in Neural Language Models. - Kristijan Armeni; Christopher J. Honey; Tal Linzen (CoNLL, 2022)

Entailment Semantics Can Be Extracted from an Ideal Language Model. - William Merrill; Alex Warstadt; Tal Linzen (CoNLL, 2022)

Causal Analysis of Syntactic Agreement Neurons in Multilingual Language Models. - Aaron Mueller; Yu Xia; Tal Linzen (CoNLL, 2022)

The MultiBERTs: BERT Reproductions for Robustness Analysis. - Thibault Sellam; Steve Yadlowsky; Ian Tenney; Jason Wei; Naomi Saphra; Alexander D'Amour; Tal Linzen; Jasmijn Bastings; Iulia Raluca Turc; Jacob Eisenstein; Dipanjan Das 001; Ellie Pavlick (ICLR, 2022)

Improving Compositional Generalization with Latent Structure and Data Augmentation. - Linlu Qiu; Peter Shaw 004; Panupong Pasupat; Pawel Krzysztof Nowak; Tal Linzen; Fei Sha; Kristina Toutanova (NAACL-HLT, 2022)

When a sentence does not introduce a discourse entity, Transformer-based models still sometimes refer to it. - Sebastian Schuster; Tal Linzen (NAACL-HLT, 2022)

Uncontrolled Lexical Exposure Leads to Overestimation of Compositional Generalization in Pretrained Models. - Najoung Kim; Tal Linzen; Paul Smolensky (CoRR, 2022)

Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark. - Nouha Dziri; Hannah Rashkin; Tal Linzen; David Reitter (Trans. Assoc. Comput. Linguistics, 2022)

How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases. - Aaron Mueller; Tal Linzen (ACL (1), 2023)

How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech. - Aditya Yedetore; Tal Linzen; Robert Frank 001; R. Thomas McCoy (ACL (1), 2023)

Verb Conjugation in Transformers Is Determined by Linear Encodings of Subject Number. - Sophie Hao; Tal Linzen (EMNLP (Findings), 2023)

SLOG: A Structural Generalization Benchmark for Semantic Parsing. - Bingzhi Li; Lucia Donatelli; Alexander Koller; Tal Linzen; Yuekun Yao; Najoung Kim (EMNLP, 2023)

A Language Model with Limited Memory Capacity Captures Interference in Human Sentence Processing. - William Timkey; Tal Linzen (EMNLP (Findings), 2023)

Language Models Can Learn Exceptions to Syntactic Rules. - Cara Su-Yi Leong; Tal Linzen (CoRR, 2023)

Do Language Models Refer? - Matthew Mandelkern; Tal Linzen (CoRR, 2023)

The Impact of Depth and Width on Transformer Language Model Generalization. - Jackson Petty; Sjoerd van Steenkiste; Ishita Dasgupta 001; Fei Sha; Dan Garrette; Tal Linzen (CoRR, 2023)

How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN. - R. Thomas McCoy; Paul Smolensky; Tal Linzen; Jianfeng Gao 001; Asli Celikyilmaz (Trans. Assoc. Comput. Linguistics, 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

Can You Learn Semantics Through Next-Word Prediction? The Case of Entailment. - William Merrill; Zhaofeng Wu; Norihito Naka; Yoon Kim; Tal Linzen (ACL (Findings), 2024)

A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models. - Tiwalayo Eisape; Michael Henry Tessler; Ishita Dasgupta 001; Fei Sha; Sjoerd van Steenkiste; Tal Linzen (NAACL-HLT, 2024)

In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax. - Aaron Mueller; Albert Webson; Jackson Petty; Tal Linzen (NAACL-HLT, 2024)

The Impact of Depth on Compositional Generalization in Transformer Language Models. - Jackson Petty; Sjoerd van Steenkiste; Ishita Dasgupta 001; Fei Sha; Dan Garrette; Tal Linzen (NAACL-HLT, 2024)

Do Language Models' Words Refer? - Matthew Mandelkern; Tal Linzen (Comput. Linguistics, 2024)

SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser. - Grusha Prasad; Tal Linzen (CoRR, 2024)

[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus. - Leshem Choshen; Ryan Cotterell; Michael Y. Hu; Tal Linzen; Aaron Mueller; Candace Ross; Alex Warstadt; Ethan Wilcox; Adina Williams; Chengxu Zhuang (CoRR, 2024)

Testing learning hypotheses using neural networks by manipulating learning data. - Cara Su-Yi Leong; Tal Linzen (CoRR, 2024)

Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora. - Michael Y. Hu; Aaron Mueller; Candace Ross; Adina Williams; Tal Linzen; Chengxu Zhuang; Ryan Cotterell; Leshem Choshen; Alex Warstadt; Ethan Gotlieb Wilcox (CoRR, 2024)

Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases. - Michael Y. Hu; Jackson Petty; Chuan Shi; William Merrill; Tal Linzen (ACL (1), 2025)

What Goes Into a LM Acceptability Judgment? Rethinking the Impact of Frequency and Length. - Lindia Tjuatja; Graham Neubig; Tal Linzen; Sophie Hao (NAACL (Long Papers), 2025)

How Does Code Pretraining Affect Language Model Task Performance? - Jackson Petty; Sjoerd van Steenkiste; Tal Linzen (Trans. Mach. Learn. Res., 2025)

BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop. - Lucas Charpentier; Leshem Choshen; Ryan Cotterell; Mustafa Omer Gul; Michael Y. Hu; Jaap Jumelet; Tal Linzen; Jing Liu; Aaron Mueller; Candace Ross; Raj Sanjay Shah; Alex Warstadt; et al. (CoRR, 2025)

Rapid Word Learning Through Meta In-Context Learning. - Wentao Wang; Guangyuan Jiang; Tal Linzen; Brenden M. Lake (CoRR, 2025)

Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models. - Linlu Qiu; Fei Sha; Kelsey R. Allen; Yoon Kim; Tal Linzen; Sjoerd van Steenkiste (CoRR, 2025)

Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora. - Alex Warstadt; Aaron Mueller; Leshem Choshen; Ethan Wilcox; Chengxu Zhuang; Juan Ciro; Rafael Mosquera; Bhargavi Paranjape; Adina Williams; Tal Linzen; Ryan Cotterell (CoRR, 2025)

Multilingual Prompting for Improving LLM Generation Diversity. - Qihan Wang; Shidong Pan; Tal Linzen; Emily Black (CoRR, 2025)

RELIC: Evaluating Compositional Instruction Following via Language Recognition. - Jackson Petty; Michael Y. Hu; Wentao Wang; Shauli Ravfogel; William Merrill; Tal Linzen (CoRR, 2025)

Beyond the Rosetta Stone: Unification Forces in Generalization Dynamics. - Carter Blum; Katja Filippova; Ann Yuan; Asma Ghandeharioun; Julian Zimmert; Fred Zhang; Jessica Hoffmann; Tal Linzen; Martin Wattenberg; Lucas Dixon; Mor Geva (CoRR, 2025)