Ruiqi Zhong
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Incoder: A generative model for code infilling and synthesis
D Fried, A Aghajanyan, J Lin, S Wang, E Wallace, F Shi, R Zhong, W Yih, ...
arXiv preprint arXiv:2204.05999, 2022
Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections
R Zhong, K Lee, Z Zhang, D Klein
EMNLP 2021, Findings, 2021
Unifiedskg: Unifying and multi-tasking structured knowledge grounding with text-to-text language models
T Xie, CH Wu, P Shi, R Zhong, T Scholak, M Yasunaga, CS Wu, M Zhong, ...
arXiv preprint arXiv:2201.05966, 2022
Fine-grained sentiment analysis with faithful attention
R Zhong, S Shao, K McKeown
arXiv preprint arXiv:1908.06870, 2019
Semantic evaluation for text-to-sql with distilled test suites
R Zhong, T Yu, D Klein
EMNLP 2020, 2020
Meta-learning via language model in-context tuning
Y Chen, R Zhong, S Zha, G Karypis, H He
arXiv preprint arXiv:2110.07814, 2021
Subspace embedding and linear regression with Orlicz norm
A Andoni, C Lin, Y Sheng, P Zhong, R Zhong
International Conference on Machine Learning, 224-233, 2018
Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level
R Zhong, D Ghosh, D Klein, J Steinhardt
ACL 2021, Findings, 2021
DS-1000: A natural and reliable benchmark for data science code generation
Y Lai, C Li, Y Wang, T Zhang, R Zhong, L Zettlemoyer, W Yih, D Fried, ...
International Conference on Machine Learning, 18319-18345, 2023
Detecting gang-involved escalation on social media using context
S Chang, R Zhong, E Adams, FT Lee, S Varia, D Patton, W Frey, C Kedzie, ...
EMNLP 2018, 2018
Approximating how single head attention learns
C Snell, R Zhong, D Klein, J Steinhardt
arXiv preprint arXiv:2103.07601, 2021
Semantic scaffolds for pseudocode-to-code generation
R Zhong, M Stern, D Klein
ACL 2020, 2020
Learning by distilling context
C Snell, D Klein, R Zhong
arXiv preprint arXiv:2209.15189, 2022
GAIA-A Multi-media Multi-lingual Knowledge Extraction and Hypothesis Generation System.
T Zhang, A Subburathinam, G Shi, L Huang, D Lu, X Pan, M Li, B Zhang, ...
TAC 2, 3, 2018
Describing differences between text distributions with natural language
R Zhong, C Snell, D Klein, J Steinhardt
International Conference on Machine Learning, 27099-27116, 2022
Goal driven discovery of distributional differences via language descriptions
R Zhong, P Zhang, S Li, J Ahn, D Klein, J Steinhardt
arXiv preprint arXiv:2302.14233, 2023
Active programming by example with a natural language prior
R Zhong, C Snell, D Klein, J Eisner
arXiv preprint arXiv:2205.12422, 2022
The effect of model size on worst-group generalization
A Pham, E Chan, V Srivatsa, D Ghosh, Y Yang, Y Yu, R Zhong, ...
arXiv preprint arXiv:2112.04094, 2021
Detecting and reducing bias in a high stakes domain
R Zhong, Y Chen, D Patton, C Selous, K McKeown
EMNLP 2019, 2019
Goal-Driven Explainable Clustering via Language Descriptions
Z Wang, J Shang, R Zhong
arXiv preprint arXiv:2305.13749, 2023
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