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Mirac Suzgun
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Scaling instruction-finetuned language models
HW Chung, L Hou, S Longpre, B Zoph, Y Tay, W Fedus, Y Li, X Wang, ...
arXiv preprint arXiv:2210.11416, 2022
16102022
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
TMLR 2023, 2022
6932022
Holistic evaluation of language models
P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ...
TMLR 2023, 2022
5672022
Challenging big-bench tasks and whether chain-of-thought can solve them
M Suzgun, N Scales, N Schärli, S Gehrmann, Y Tay, HW Chung, ...
ACL 2023 (Findings), 2022
2702022
Language models are multilingual chain-of-thought reasoners
F Shi, M Suzgun, M Freitag, X Wang, S Srivats, S Vosoughi, HW Chung, ...
ICLR 2023, 2022
1302022
LSTM Networks Can Perform Dynamic Counting
M Suzgun, S Gehrmann, Y Belinkov, SM Shieber
ACL 2019 Workshop on Deep Learning and Formal Languages, 2019
612019
H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. Scaling instruction-finetuned language models
HW Chung, L Hou, S Longpre, B Zoph, Y Tay, W Fedus, Y Li, X Wang, ...
arXiv preprint arXiv:2210.11416 6 (7), 2022
442022
Do Language Models Know When They're Hallucinating References?
A Agrawal, M Suzgun, L Mackey, AT Kalai
arXiv preprint arXiv:2305.18248, 2023
402023
Prompt-and-rerank: A method for zero-shot and few-shot arbitrary textual style transfer with small language models
M Suzgun, L Melas-Kyriazi, D Jurafsky
EMNLP 2022, 2022
362022
On Evaluating the Generalization of LSTM Models in Formal Languages
M Suzgun, Y Belinkov, SM Shieber
SCiL 2019, 2018
352018
Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languages
M Suzgun, S Gehrmann, Y Belinkov, SM Shieber
arXiv preprint arXiv:1911.03329, 2019
342019
HospiSign: An Interactive Sign Language Platform for Hearing Impaired
M Suzgun, H Ozdemir, N Camgoz, A Kindiroglu, D Basaran, C Togay, ...
Journal of Naval Sciences and Engineering 11 (3), 75-92, 2015
272015
Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study
T Zack, E Lehman, M Suzgun, JA Rodriguez, LA Celi, J Gichoya, ...
The Lancet Digital Health 6 (1), e12-e22, 2024
262024
Follow the wisdom of the crowd: Effective text generation via minimum bayes risk decoding
M Suzgun, L Melas-Kyriazi, D Jurafsky
ACL 2023, 2022
192022
Safety-tuned llamas: Lessons from improving the safety of large language models that follow instructions
F Bianchi, M Suzgun, G Attanasio, P Röttger, D Jurafsky, T Hashimoto, ...
arXiv preprint arXiv:2309.07875, 2023
182023
When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization
F Ladhak, E Durmus, M Suzgun, T Zhang, D Jurafsky, K Mckeown, ...
EACL 2023, 0
15*
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
M Suzgun, L Melas-Kyriazi, SK Sarkar, SD Kominers, SM Shieber
NeurIPS 2023 (Datasets and Benchmarks Track), 2022
102022
Large legal fictions: Profiling legal hallucinations in large language models
M Dahl, V Magesh, M Suzgun, DE Ho
arXiv preprint arXiv:2401.01301, 2024
82024
Meta-prompting: Enhancing language models with task-agnostic scaffolding
M Suzgun, AT Kalai
arXiv preprint arXiv:2401.12954, 2024
72024
Coding Inequity: Assessing GPT-4's Potential for Perpetuating Racial and Gender Biases in Healthcare
T Zack, E Lehman, M Suzgun, JA Rodriguez, LA Celi, J Gichoya, ...
medRxiv, 2023.07. 13.23292577, 2023
72023
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