TAG Parsing with Neural Networks and Vector Representations of Supertags. - Jungo Kasai; Bob Frank; R. Thomas McCoy; Owen Rambow; Alexis Nasr (EMNLP, 2017)

Linguistically Rich Vector Representations of Supertags for TAG Parsing. - Dan Friedman; Jungo Kasai; R. Thomas McCoy; Robert Frank 001; Forrest Davis; Owen Rambow (TAG, 2017)

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)

Parser combinators for Tigrinya and Oromo morphology. - Patrick Littell; Tom McCoy 001; Na-Rae Han; Shruti Rijhwani; Zaid Sheikh; David R. Mortensen; Teruko Mitamura; Lori S. Levin (LREC, 2018)

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

Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling. - Samuel R. Bowman; Ellie Pavlick; Edouard Grave; Benjamin Van Durme; Alex Wang; Jan Hula; Patrick Xia 002; Raghavendra Pappagari; R. Thomas McCoy; Roma Patel; Najoung Kim; Ian Tenney; et al. (CoRR, 2018)

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

Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling. - Alex Wang; Jan Hula; Patrick Xia 002; Raghavendra Pappagari; R. Thomas McCoy; Roma Patel; Najoung Kim; Ian Tenney; Yinghui Huang; Katherin Yu; Shuning Jin; Berlin Chen; et al. (ACL (1), 2019)

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

What do you learn from context? Probing for sentence structure in contextualized word representations. - Ian Tenney; Patrick Xia 002; Berlin Chen; Alex Wang; Adam Poliak; R. Thomas McCoy; Najoung Kim; Benjamin Van Durme; Samuel R. Bowman; Dipanjan Das 001; Ellie Pavlick (ICLR (Poster), 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)

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

Syntactic Data Augmentation Increases Robustness to Inference Heuristics. - Junghyun Min; R. Thomas McCoy; Dipanjan Das 001; Emily Pitler; 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)

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

Picking BERT's Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis. - Michael A. Lepori; R. Thomas McCoy (COLING, 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)

Infinite use of finite means? Evaluating the generalization of center embedding learned from an artificial grammar. - Richard Thomas McCoy; Jennifer Culbertson; Paul Smolensky; Geraldine Legendre (CogSci, 2021)

Neurocompositional Computing: From the Central Paradox of Cognition to a New Generation of AI Systems. - Paul Smolensky; Richard Thomas McCoy; Roland Fernandez; Matthew Goldrick 001; Jianfeng Gao 001 (AI Mag., 2022)

Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages. - Paul Soulos; Sudha Rao; Caitlin Smith; Eric Rosen; Asli Celikyilmaz; R. Thomas McCoy; Yichen Jiang; Coleman Haley; Roland Fernandez; Hamid Palangi; Jianfeng Gao 001; Paul Smolensky (CoRR, 2022)

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)

Modeling rapid language learning by distilling Bayesian priors into artificial neural networks. - R. Thomas McCoy; Thomas L. Griffiths 001 (CoRR, 2023)

Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve. - R. Thomas McCoy; Shunyu Yao; Dan Friedman; Matthew Hardy; Thomas L. Griffiths 001 (CoRR, 2023)

Bayes in the age of intelligent machines. - Thomas L. Griffiths 001; Jian-Qiao Zhu; Erin Grant; R. Thomas McCoy (CoRR, 2023)

Deep de Finetti: Recovering Topic Distributions from Large Language Models. - Liyi Zhang; R. Thomas McCoy; Theodore R. Sumers; Jian-Qiao Zhu; Thomas L. Griffiths 001 (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)

Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning. - Akshara Prabhakar; Thomas L. Griffiths 001; R. Thomas McCoy (EMNLP (Findings), 2024)

ModeLing: A Novel Dataset for Testing Linguistic Reasoning in Language Models. - Nathan A. Chi; Teodor Malchev; Riley Kong; Ryan Chi; Lucas Huang; Ethan A. Chi; R. Thomas McCoy; Dragomir Radev (SIGTYPE, 2024)

Distilling Symbolic Priors for Concept Learning into Neural Networks. - Ioana Marinescu; R. Thomas McCoy; Thomas L. Griffiths 001 (CoRR, 2024)

modeLing: A Novel Dataset for Testing Linguistic Reasoning in Language Models. - Nathan A. Chi; Teodor Malchev; Riley Kong; Ryan A. Chi; Lucas Huang; Ethan A. Chi; R. Thomas McCoy; Dragomir Radev (CoRR, 2024)

Is In-Context Learning a Type of Gradient-Based Learning? Evidence from the Inverse Frequency Effect in Structural Priming. - Zhenghao Zhou; Robert Frank 001; R. Thomas McCoy (CoRR, 2024)

When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1. - R. Thomas McCoy; Shunyu Yao; Dan Friedman; Mathew D. Hardy; Thomas L. Griffiths 001 (CoRR, 2024)

Minimization of Boolean Complexity in In-Context Concept Learning. - Leroy Z. Wang; R. Thomas McCoy; Shane Steinert-Threlkeld (CoRR, 2024)

Is In-Context Learning a Type of Error-Driven Learning? Evidence from the Inverse Frequency Effect in Structural Priming. - Zhenghao Zhou; Robert Frank 001; R. Thomas McCoy (NAACL (Long Papers), 2025)

What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions. - Liyi Zhang; Michael Y. Li; R. Thomas McCoy; Theodore R. Sumers; Jian-Qiao Zhu; Thomas L. Griffiths 001 (Trans. Mach. Learn. Res., 2025)

Teasing Apart Architecture and Initial Weights as Sources of Inductive Bias in Neural Networks. - Gianluca M. Bencomo; Max Gupta; Ioana Marinescu; R. Thomas McCoy; Thomas L. Griffiths 001 (CoRR, 2025)

Using the Tools of Cognitive Science to Understand Large Language Models at Different Levels of Analysis. - Alexander Ku; Declan Campbell; Xuechunzi Bai; Jiayi Geng; Ryan Liu 001; Raja Marjieh; R. Thomas McCoy; Andrew Nam; Ilia Sucholutsky; Veniamin Veselovsky; Liyi Zhang; Jian-Qiao Zhu; et al. (CoRR, 2025)

Convolutional Neural Networks Can (Meta-)Learn the Same-Different Relation. - Max Gupta; Sunayana Rane; R. Thomas McCoy; Thomas L. Griffiths 001 (CoRR, 2025)

Identifying and Mitigating the Influence of the Prior Distribution in Large Language Models. - Liyi Zhang; Veniamin Veselovsky; R. Thomas McCoy; Thomas L. Griffiths 001 (CoRR, 2025)

Creativity or Brute Force? Using Brainteasers as a Window into the Problem-Solving Abilities of Large Language Models. - Simeng Han; Stephen Xia; Grant Zhang; Howard Dai; Chen Liu; Lichang Chen; Hoang Huy Nguyen; Hongyuan Mei; Jiayuan Mao; R. Thomas McCoy (CoRR, 2025)

Learning to Reason via Mixture-of-Thought for Logical Reasoning. - Tong Zheng; Lichang Chen; Simeng Han; R. Thomas McCoy; Heng Huang (CoRR, 2025)

Whither symbols in the era of advanced neural networks? - Thomas L. Griffiths 001; Brenden M. Lake; R. Thomas McCoy; Ellie Pavlick; Taylor W. Webb (CoRR, 2025)

The Structural Sources of Verb Meaning Revisited: Large Language Models Display Syntactic Bootstrapping. - Xiaomeng Zhu; R. Thomas McCoy; Robert Frank (CoRR, 2025)