McCoy, R. Thomas

What do you learn from context? Probing for sentence structure in contextualized word representations

Tenney, I., Xia, P., Chen, B., Wang, A., Poliak, A., McCoy, R. T., Kim, N., Van Durme, B., Bowman, S. R., Das, D., et al. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 698

Embers of autoregression show how large language models are shaped by the problem they are trained to solve

McCoy, R. T., Yao, S., Friedman, D., Hardy, M. D., & Griffiths, T. L. (2024) · Proceedings of the National Academy of Sciences

Topic ModelingNatural Language Processing Techniques

Cited by 96

How much do language models copy from their training data? evaluating linguistic novelty in text generation using RAVEN

McCoy, R. T., Smolensky, P., Linzen, T., Gao, J., & Celikyilmaz, A. (2023) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 70

How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN

McCoy, R. T., Smolensky, P., Linzen, T., Gao, J., & Celikyilmaz, A. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 70

Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve

McCoy, R. T., Yao, S., Friedman, D., Hardy, M., & Griffiths, T. L. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 37

Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning

Prabhakar, A., Griffiths, T. L., & McCoy, R. T. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024

Topic ModelingNatural Language Processing Techniques

Cited by 14

Universal linguistic inductive biases via meta-learning

McCoy, R. T., Grant, E., Smolensky, P., Griffiths, T. L., & Linzen, T. (2020) · arXiv

Language Development and DisordersDomain Adaptation and Few-Shot Learning

Cited by 13