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Conference and Workshop Papers
- 2024
[c18]Jiaxin Wen, Ruiqi Zhong, Pei Ke, Zhihong Shao, Hongning Wang, Minlie Huang:
Learning Task Decomposition to Assist Humans in Competitive Programming. ACL (1) 2024: 11700-11723- [c17]Lisa Dunlap, Yuhui Zhang, Xiaohan Wang, Ruiqi Zhong, Trevor Darrell, Jacob Steinhardt, Joseph E. Gonzalez, Serena Yeung-Levy:
Describing Differences in Image Sets with Natural Language. CVPR 2024: 24199-24208 - [c16]Yanda Chen, Ruiqi Zhong, Narutatsu Ri, Chen Zhao, He He, Jacob Steinhardt, Zhou Yu, Kathleen R. McKeown:
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations. ICML 2024 - 2023
- [c15]Ruiqi Zhong, Charlie Snell, Dan Klein, Jason Eisner:
Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL. EMNLP 2023: 5126-5152 - [c14]Zihan Wang, Jingbo Shang, Ruiqi Zhong:
Goal-Driven Explainable Clustering via Language Descriptions. EMNLP 2023: 10626-10649 - [c13]Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, Mike Lewis:
InCoder: A Generative Model for Code Infilling and Synthesis. ICLR 2023 - [c12]Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Wen-Tau Yih, Daniel Fried, Sida I. Wang, Tao Yu:
DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation. ICML 2023: 18319-18345 - [c11]Ruiqi Zhong, Peter Zhang, Steve Li, Jinwoo Ahn, Dan Klein, Jacob Steinhardt:
Goal Driven Discovery of Distributional Differences via Language Descriptions. NeurIPS 2023 - 2022
- [c10]Yanda Chen, Ruiqi Zhong, Sheng Zha, George Karypis, He He:
Meta-learning via Language Model In-context Tuning. ACL (1) 2022: 719-730 - [c9]Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu:
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models. EMNLP 2022: 602-631 - [c8]Ruiqi Zhong, Charlie Snell, Dan Klein, Jacob Steinhardt:
Describing Differences between Text Distributions with Natural Language. ICML 2022: 27099-27116 - 2021
- [c7]Ruiqi Zhong, Dhruba Ghosh, Dan Klein, Jacob Steinhardt:
Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level. ACL/IJCNLP (Findings) 2021: 3813-3827 - [c6]Ruiqi Zhong, Kristy Lee, Zheng Zhang, Dan Klein:
Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections. EMNLP (Findings) 2021: 2856-2878 - 2020
- [c5]Ruiqi Zhong, Mitchell Stern, Dan Klein:
Semantic Scaffolds for Pseudocode-to-Code Generation. ACL 2020: 2283-2295 - [c4]Ruiqi Zhong, Tao Yu, Dan Klein:
Semantic Evaluation for Text-to-SQL with Distilled Test Suites. EMNLP (1) 2020: 396-411 - 2019
- [c3]Ruiqi Zhong, Yanda Chen, Desmond Patton, Charlotte Selous, Kathy McKeown:
Detecting and Reducing Bias in a High Stakes Domain. EMNLP/IJCNLP (1) 2019: 4764-4774 - 2018
- [c2]Serina Chang, Ruiqi Zhong, Ethan Adams, Fei-Tzin Lee, Siddharth Varia, Desmond Patton, William R. Frey, Chris Kedzie, Kathy McKeown:
Detecting Gang-Involved Escalation on Social Media Using Context. EMNLP 2018: 46-56 - [c1]Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong:
Subspace Embedding and Linear Regression with Orlicz Norm. ICML 2018: 224-233
Informal and Other Publications
- 2024
- [i26]Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, José Hernández-Orallo, Lewis Hammond, Eric J. Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Yoshua Bengio, Danqi Chen, Samuel Albanie, Tegan Maharaj, Jakob N. Foerster, Florian Tramèr, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger:
Foundational Challenges in Assuring Alignment and Safety of Large Language Models. CoRR abs/2404.09932 (2024) - [i25]Jiaxin Wen, Ruiqi Zhong, Pei Ke, Zhihong Shao, Hongning Wang, Minlie Huang:
Learning Task Decomposition to Assist Humans in Competitive Programming. CoRR abs/2406.04604 (2024) - [i24]Ruiqi Zhong, Heng Wang, Dan Klein, Jacob Steinhardt:
Explaining Datasets in Words: Statistical Models with Natural Language Parameters. CoRR abs/2409.08466 (2024) - [i23]Jiaxin Wen, Ruiqi Zhong, Akbir Khan, Ethan Perez, Jacob Steinhardt, Minlie Huang, Samuel R. Bowman, He He, Shi Feng:
Language Models Learn to Mislead Humans via RLHF. CoRR abs/2409.12822 (2024) - 2023
- [i22]Ruiqi Zhong, Peter Zhang
, Steve Li, Jinwoo Ahn, Dan Klein, Jacob Steinhardt:
Goal Driven Discovery of Distributional Differences via Language Descriptions. CoRR abs/2302.14233 (2023) - [i21]Zihan Wang, Jingbo Shang, Ruiqi Zhong:
Goal-Driven Explainable Clustering via Language Descriptions. CoRR abs/2305.13749 (2023) - [i20]Yanda Chen, Ruiqi Zhong, Narutatsu Ri, Chen Zhao, He He, Jacob Steinhardt, Zhou Yu, Kathleen R. McKeown:
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations. CoRR abs/2307.08678 (2023) - [i19]Lisa Dunlap, Yuhui Zhang, Xiaohan Wang, Ruiqi Zhong, Trevor Darrell, Jacob Steinhardt, Joseph E. Gonzalez, Serena Yeung-Levy:
Describing Differences in Image Sets with Natural Language. CoRR abs/2312.02974 (2023) - 2022
- [i18]Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir R. Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu:
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models. CoRR abs/2201.05966 (2022) - [i17]Ruiqi Zhong, Charlie Snell, Dan Klein, Jacob Steinhardt:
Summarizing Differences between Text Distributions with Natural Language. CoRR abs/2201.12323 (2022) - [i16]Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, Mike Lewis:
InCoder: A Generative Model for Code Infilling and Synthesis. CoRR abs/2204.05999 (2022) - [i15]Ruiqi Zhong, Charlie Snell, Dan Klein, Jason Eisner:
Active Programming by Example with a Natural Language Prior. CoRR abs/2205.12422 (2022) - [i14]Charlie Snell, Dan Klein, Ruiqi Zhong:
Learning by Distilling Context. CoRR abs/2209.15189 (2022) - [i13]Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Scott Wen-tau Yih, Daniel Fried, Sida I. Wang, Tao Yu:
DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation. CoRR abs/2211.11501 (2022) - 2021
- [i12]Charlie Snell, Ruiqi Zhong, Dan Klein, Jacob Steinhardt:
Approximating How Single Head Attention Learns. CoRR abs/2103.07601 (2021) - [i11]Ruiqi Zhong, Kristy Lee, Zheng Zhang, Dan Klein:
Meta-tuning Language Models to Answer Prompts Better. CoRR abs/2104.04670 (2021) - [i10]Ruiqi Zhong, Dhruba Ghosh, Dan Klein, Jacob Steinhardt:
Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level. CoRR abs/2105.06020 (2021) - [i9]Yanda Chen, Ruiqi Zhong, Sheng Zha, George Karypis, He He:
Meta-learning via Language Model In-context Tuning. CoRR abs/2110.07814 (2021) - [i8]Alan Pham, Eunice Chan, Vikranth Srivatsa, Dhruba Ghosh, Yaoqing Yang, Yaodong Yu, Ruiqi Zhong, Joseph E. Gonzalez, Jacob Steinhardt:
The Effect of Model Size on Worst-Group Generalization. CoRR abs/2112.04094 (2021) - 2020
- [i7]Ruiqi Zhong, Mitchell Stern, Dan Klein:
Semantic Scaffolds for Pseudocode-to-Code Generation. CoRR abs/2005.05927 (2020) - [i6]Ruiqi Zhong, Tao Yu, Dan Klein:
Semantic Evaluation for Text-to-SQL with Distilled Test Suites. CoRR abs/2010.02840 (2020) - 2019
- [i5]Ruiqi Zhong, Steven Shao, Kathleen R. McKeown:
Fine-grained Sentiment Analysis with Faithful Attention. CoRR abs/1908.06870 (2019) - [i4]Ruiqi Zhong, Yanda Chen, Desmond Patton, Charlotte Selous, Kathy McKeown:
Detecting and Reducing Bias in a High Stakes Domain. CoRR abs/1908.11474 (2019) - 2018
- [i3]Tongtao Zhang, Ananya Subburathinam, Ge Shi, Lifu Huang, Di Lu, Xiaoman Pan, Manling Li, Boliang Zhang, Qingyun Wang, Spencer Whitehead, Heng Ji, Alireza Zareian, Hassan Akbari, Brian Chen, Ruiqi Zhong, Steven Shao, Emily Allaway, Shih-Fu Chang, Kathleen R. McKeown, Dongyu Li, Xin Huang, Kexuan Sun, Xujun Peng, Ryan Gabbard, Marjorie Freedman, Mayank Kejriwal, Ram Nevatia, Pedro A. Szekely, T. K. Satish Kumar, Ali Sadeghian, Giacomo Bergami, Sourav Dutta, Miguel E. Rodríguez, Daisy Zhe Wang:
GAIA - A Multi-media Multi-lingual Knowledge Extraction and Hypothesis Generation System. TAC 2018 - [i2]Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong:
Subspace Embedding and Linear Regression with Orlicz Norm. CoRR abs/1806.06430 (2018) - [i1]Serina Chang, Ruiqi Zhong, Ethan Adams, Fei-Tzin Lee, Siddharth Varia, Desmond Patton, William R. Frey, Chris Kedzie, Kathy McKeown:
Detecting Gang-Involved Escalation on Social Media Using Context. CoRR abs/1809.03632 (2018)
Coauthor Index
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