Grusha Prasad
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Dynabench: Rethinking benchmarking in NLP
D Kiela, M Bartolo, Y Nie, D Kaushik, A Geiger, Z Wu, B Vidgen, G Prasad, ...
arXiv preprint arXiv:2104.14337, 2021
Using priming to uncover the organization of syntactic representations in neural language models
G Prasad, M Van Schijndel, T Linzen
arXiv preprint arXiv:1909.10579, 2019
Counterfactual interventions reveal the causal effect of relative clause representations on agreement prediction
S Ravfogel, G Prasad, T Linzen, Y Goldberg
arXiv preprint arXiv:2105.06965, 2021
Rapid syntactic adaptation in self-paced reading: Detectable, but only with many participants.
G Prasad, T Linzen
Journal of Experimental Psychology: Learning, Memory, and Cognition 47 (7), 1156, 2021
To what extent do human explanations of model behavior align with actual model behavior?
G Prasad, Y Nie, M Bansal, R Jia, D Kiela, A Williams
arXiv preprint arXiv:2012.13354, 2020
Surprisal does not explain syntactic disambiguation difficulty: evidence from a large-scale benchmark
KJ Huang, S Arehalli, M Kugemoto, C Muxica, G Prasad, B Dillon, ...
PsyArXiv, 2023
Do self-paced reading studies provide evidence for rapid syntactic adaptation
G Prasad, T Linzen
PsyArXiv preprint PsyArXiv: 10.31234/osf. io/9ptg4, 2019
The P600 for singular “they”: How the brain reacts when John decides to treat themselves to sushi.
G Prasad, J Morris
PsyArXiv, 2018
How much harder are hard garden-path sentences than easy ones?
G Prasad, T Linzen
CogSci, 3339, 2019
SPR mega-benchmark shows surprisal tracks construction-but not item-level difficulty
KJ Huang, S Arehalli, M Kugemoto, C Muxica, B Dillon, T Linzen
35th Annual Conference on Human Sentence Processing, Santa Cruz, California …, 2022
Reassessing the evidence for syntactic adaptation from self-paced reading studies
G Prasad, T Linzen
Poster Session, 32nd CUNY Conference on Human Sentence Processing, Boulder …, 2019
The P600 for Singular'they': How the Brain Reacts when John Decides to Treat Themselves to Sushi
G Prasad
Hampshire College, 2017
Can training neural language models on a curriculum with developmentally plausible data improve alignment with human reading behavior?
A Chobey, O Smith, A Wang, G Prasad
arXiv preprint arXiv:2311.18761, 2023
Large-scale benchmark yields no evidence that language model surprisal explains syntactic disambiguation difficulty
KJ Huang, S Arehalli, M Kugemoto, C Muxica, G Prasad, B Dillon, ...
Journal of Memory and Language 137, 104510, 2024
SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser
G Prasad, T Linzen
arXiv preprint arXiv:2403.07202, 2024
The Promises and Pitfalls of Large Language Models for Science and Society
A Kim, G Prasad
2024 AAAS Annual Meeting, 2024
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Articles 1–16