Detecting Gang-Involved Escalation on Social Media Using Context. - Serina Chang; Ruiqi Zhong; Ethan Adams; Fei-Tzin Lee; Siddharth Varia; Desmond Patton; William R. Frey; Chris Kedzie; Kathy McKeown (EMNLP, 2018)

Subspace Embedding and Linear Regression with Orlicz Norm. - Alexandr Andoni; Chengyu Lin 001; Ying Sheng 004; Peilin Zhong; Ruiqi Zhong (ICML, 2018)

GAIA - A Multi-media Multi-lingual Knowledge Extraction and Hypothesis Generation System. - Tongtao Zhang; Ananya Subburathinam; Ge Shi 002; Lifu Huang; Di Lu 003; Xiaoman Pan; Manling Li; Boliang Zhang; Qingyun Wang 005; Spencer Whitehead; Heng Ji 001; Alireza Zareian; et al. (TAC, 2018)

Detecting and Reducing Bias in a High Stakes Domain. - Ruiqi Zhong; Yanda Chen; Desmond Patton; Charlotte Selous; Kathy McKeown (EMNLP/IJCNLP (1), 2019)

Fine-grained Sentiment Analysis with Faithful Attention. - Ruiqi Zhong; Steven Shao; Kathleen R. McKeown (CoRR, 2019)

Semantic Scaffolds for Pseudocode-to-Code Generation. - Ruiqi Zhong; Mitchell Stern; Dan Klein 001 (ACL, 2020)

Semantic Evaluation for Text-to-SQL with Distilled Test Suites. - Ruiqi Zhong; Tao Yu 009; Dan Klein 001 (EMNLP (1), 2020)

Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level. - Ruiqi Zhong; Dhruba Ghosh; Dan Klein 001; Jacob Steinhardt (ACL/IJCNLP (Findings), 2021)

Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections. - Ruiqi Zhong; Kristy Lee; Zheng Zhang; Dan Klein 001 (EMNLP (Findings), 2021)

Approximating How Single Head Attention Learns. - Charlie Snell; Ruiqi Zhong; Dan Klein 001; Jacob Steinhardt (CoRR, 2021)

Meta-tuning Language Models to Answer Prompts Better. - Ruiqi Zhong; Kristy Lee; Zheng Zhang; Dan Klein 001 (CoRR, 2021)

The Effect of Model Size on Worst-Group Generalization. - Alan Pham; Eunice Chan; Vikranth Srivatsa; Dhruba Ghosh; Yaoqing Yang; Yaodong Yu; Ruiqi Zhong; Joseph E. Gonzalez; Jacob Steinhardt (CoRR, 2021)

Meta-learning via Language Model In-context Tuning. - Yanda Chen; Ruiqi Zhong; Sheng Zha; George Karypis; He He 001 (ACL (1), 2022)

UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models. - Tianbao Xie; Chen Henry Wu; Peng Shi 010; Ruiqi Zhong; Torsten Scholak; Michihiro Yasunaga; Chien-Sheng Wu; Ming Zhong 005; Pengcheng Yin; Sida I. Wang; Victor Zhong; Bailin Wang; et al. (EMNLP, 2022)

Describing Differences between Text Distributions with Natural Language. - Ruiqi Zhong; Charlie Snell; Dan Klein 001; Jacob Steinhardt (ICML, 2022)

Summarizing Differences between Text Distributions with Natural Language. - Ruiqi Zhong; Charlie Snell; Dan Klein 001; Jacob Steinhardt (CoRR, 2022)

Active Programming by Example with a Natural Language Prior. - Ruiqi Zhong; Charlie Snell; Dan Klein 001; Jason Eisner (CoRR, 2022)

Learning by Distilling Context. - Charlie Snell; Dan Klein 001; Ruiqi Zhong (CoRR, 2022)

Goal-Driven Explainable Clustering via Language Descriptions. - Zihan Wang 001; Jingbo Shang; Ruiqi Zhong (EMNLP, 2023)

Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQL. - Ruiqi Zhong; Charlie Snell; Dan Klein 001; Jason Eisner (EMNLP, 2023)

InCoder: A Generative Model for Code Infilling and Synthesis. - Daniel Fried; Armen Aghajanyan; Jessy Lin; Sida Wang 001; Eric Wallace; Freda Shi; Ruiqi Zhong; Scott Yih; Luke Zettlemoyer; Mike Lewis (ICLR, 2023)

DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation. - Yuhang Lai; Chengxi Li 011; Yiming Wang; Tianyi Zhang; Ruiqi Zhong; Luke Zettlemoyer; Wen-Tau Yih; Daniel Fried; Sida I. Wang; Tao Yu 009 (ICML, 2023)

Goal Driven Discovery of Distributional Differences via Language Descriptions. - Ruiqi Zhong; Peter Zhang; Steve Li; Jinwoo Ahn; Dan Klein 001; Jacob Steinhardt (NeurIPS, 2023)

Learning Task Decomposition to Assist Humans in Competitive Programming. - Jiaxin Wen; Ruiqi Zhong; Pei Ke; Zhihong Shao; Hongning Wang; Minlie Huang (ACL (1), 2024)

Describing Differences in Image Sets with Natural Language. - Lisa Dunlap; Yuhui Zhang; Xiaohan Wang; Ruiqi Zhong; Trevor Darrell; Jacob Steinhardt; Joseph E. Gonzalez; Serena Yeung-Levy (CVPR, 2024)

Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations. - Yanda Chen; Ruiqi Zhong; Narutatsu Ri; Chen Zhao; He He 001; Jacob Steinhardt; Zhou Yu; Kathleen R. McKeown (ICML, 2024)

Explaining Datasets in Words: Statistical Models with Natural Language Parameters. - Ruiqi Zhong; Heng Wang; Dan Klein 001; Jacob Steinhardt (NeurIPS, 2024)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models. - 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; et al. (CoRR, 2024)

Foundational Challenges in Assuring Alignment and Safety of Large Language Models. - 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; et al. (Trans. Mach. Learn. Res., 2024)

Language Models Learn to Mislead Humans via RLHF. - Jiaxin Wen; Ruiqi Zhong; Akbir Khan; Ethan Perez; Jacob Steinhardt; Minlie Huang; Samuel R. Bowman; He He 001; Shi Feng 005 (ICLR, 2025)

Language Models can Categorize System Inputs for Performance Analysis. - Dominic Sobhani; Ruiqi Zhong; Edison Marrese-Taylor; Keisuke Sakaguchi; Yutaka Matsuo (NAACL (Long Papers), 2025)

Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming. - Mrinank Sharma; Meg Tong; Jesse Mu; Jerry Wei; Jorrit Kruthoff; Scott Goodfriend; Euan Ong; Alwin Peng; Raj Agarwal; Cem Anil; Amanda Askell; Nathan Bailey; et al. (CoRR, 2025)