Data Recombination for Neural Semantic Parsing. - Robin Jia; Percy Liang (ACL (1), 2016)

Adversarial Examples for Evaluating Reading Comprehension Systems. - Robin Jia; Percy Liang (EMNLP, 2017)

Learning concepts through conversations in spoken dialogue systems. - Robin Jia; Larry P. Heck; Dilek Hakkani-Tür; Georgi Nikolov (ICASSP, 2017)

Know What You Don't Know: Unanswerable Questions for SQuAD. - Pranav Rajpurkar; Robin Jia; Percy Liang (ACL (2), 2018)

Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer. - Juncen Li; Robin Jia; He He 001; Percy Liang (NAACL-HLT, 2018)

MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension. - Adam Fisch; Alon Talmor; Robin Jia; Minjoon Seo; Eunsol Choi; Danqi Chen 001 (MRQA@EMNLP, 2019)

Certified Robustness to Adversarial Word Substitutions. - Robin Jia; Aditi Raghunathan; Kerem Göksel; Percy Liang (EMNLP/IJCNLP (1), 2019)

Document-Level N-ary Relation Extraction with Multiscale Representation Learning. - Robin Jia; Cliff Wong; Hoifung Poon (NAACL-HLT (1), 2019)

Robust Encodings: A Framework for Combating Adversarial Typos. - Erik Jones; Robin Jia; Aditi Raghunathan; Percy Liang (ACL, 2020)

Selective Question Answering under Domain Shift. - Amita Kamath; Robin Jia; Percy Liang (ACL, 2020)

With Little Power Comes Great Responsibility. - Dallas Card; Peter Henderson 002; Urvashi Khandelwal; Robin Jia; Kyle Mahowald; Dan Jurafsky (EMNLP (1), 2020)

On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks. - Stephen Mussmann; Robin Jia; Percy Liang (EMNLP (Findings), 2020)

Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA. - Ana Valeria Gonzalez; Gagan Bansal; Angela Fan; Robin Jia; Yashar Mehdad; Srinivasan Iyer 001 (CoRR, 2020)

Building robust natural language processing systems. - Robin Jia (2020)

Do Explanations Help Users Detect Errors in Open-Domain QA? An Evaluation of Spoken vs. Visual Explanations. - Ana Valeria Gonzalez; Gagan Bansal; Angela Fan; Yashar Mehdad; Robin Jia; Srinivasan Iyer 001 (ACL/IJCNLP (Findings), 2021)

Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards? - Pedro Rodriguez 001; Joe Barrow; Alexander Miserlis Hoyle; John P. Lalor; Robin Jia; Jordan L. Boyd-Graber (ACL/IJCNLP (1), 2021)

The statistical advantage of automatic NLG metrics at the system level. - Johnny Tian-Zheng Wei; Robin Jia (ACL/IJCNLP (1), 2021)

To what extent do human explanations of model behavior align with actual model behavior? - Grusha Prasad; Yixin Nie; Mohit Bansal; Robin Jia; Douwe Kiela; Adina Williams (BlackboxNLP@EMNLP, 2021)

Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation. - Max Bartolo; Tristan Thrush; Robin Jia; Sebastian Riedel 001; Pontus Stenetorp; Douwe Kiela (EMNLP (1), 2021)

Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little. - Koustuv Sinha; Robin Jia; Dieuwke Hupkes; Joelle Pineau; Adina Williams; Douwe Kiela (EMNLP (1), 2021)

Dynabench: Rethinking Benchmarking in NLP. - Douwe Kiela; Max Bartolo; Yixin Nie; Divyansh Kaushik; Atticus Geiger; Zhengxuan Wu; Bertie Vidgen; Grusha Prasad; Amanpreet Singh; Pratik Ringshia; Zhiyi Ma; Tristan Thrush; et al. (NAACL-HLT, 2021)

Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality. - Mina Lee 002; Chris Donahue; Robin Jia; Alexander Iyabor; Percy Liang (NAACL-HLT, 2021)

Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking. - Zhiyi Ma; Kawin Ethayarajh; Tristan Thrush; Somya Jain; Ledell Wu; Robin Jia; Christopher Potts; Adina Williams; Douwe Kiela (NeurIPS, 2021)

Can Small and Synthetic Benchmarks Drive Modeling Innovation? A Retrospective Study of Question Answering Modeling Approaches. - Nelson F. Liu; Tony Lee; Robin Jia; Percy Liang (CoRR, 2021)

Question Answering Infused Pre-training of General-Purpose Contextualized Representations. - Robin Jia; Mike Lewis; Luke Zettlemoyer (ACL (Findings), 2022)

On Continual Model Refinement in Out-of-Distribution Data Streams. - Bill Yuchen Lin; Sida Wang 001; Xi Victoria Lin; Robin Jia; Lin Xiao; Xiang Ren 001; Scott Yih (ACL (1), 2022)

Analyzing Dynamic Adversarial Training Data in the Limit. - Eric Wallace; Adina Williams; Robin Jia; Douwe Kiela (ACL (Findings), 2022)

Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems. - Wang Zhu 001; Jesse Thomason; Robin Jia (EMNLP (Findings), 2022)

Knowledge Base Question Answering by Case-based Reasoning over Subgraphs. - Rajarshi Das; Ameya Godbole; Ankita Naik; Elliot Tower; Manzil Zaheer; Hannaneh Hajishirzi; Robin Jia; Andrew McCallum (ICML, 2022)

Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants. - Max Bartolo; Tristan Thrush; Sebastian Riedel 001; Pontus Stenetorp; Robin Jia; Douwe Kiela (NAACL-HLT, 2022)

On the Robustness of Reading Comprehension Models to Entity Renaming. - Jun Yan 012; Yang Xiao; Sagnik Mukherjee; Bill Yuchen Lin; Robin Jia; Xiang Ren 001 (NAACL-HLT, 2022)

CoNAL: Anticipating Outliers with Large Language Models. - Albert Xu; Xiang Ren 001; Robin Jia (CoRR, 2022)

Careful Data Curation Stabilizes In-context Learning. - Ting-Yun Chang; Robin Jia (CoRR, 2022)

Data Curation Alone Can Stabilize In-context Learning. - Ting-Yun Chang; Robin Jia (ACL (1), 2023)

Are Sample-Efficient NLP Models More Robust? - Nelson F. Liu; Ananya Kumar; Percy Liang; Robin Jia (ACL (2), 2023)

Do Question Answering Modeling Improvements Hold Across Benchmarks? - Nelson F. Liu; Tony Lee; Robin Jia; Percy Liang (ACL (1), 2023)

Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language Models. - Albert Xu; Xiang Ren 001; Robin Jia (ACL (1), 2023)

Benchmarking Long-tail Generalization with Likelihood Splits. - Ameya Godbole; Robin Jia (EACL (Findings), 2023)

Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering. - Wang Zhu 001; Jesse Thomason; Robin Jia (EMNLP, 2023)

SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples. - Deqing Fu; Ameya Godbole; Robin Jia (EMNLP, 2023)

Estimating Large Language Model Capabilities without Labeled Test Data. - Harvey Yiyun Fu; Qinyuan Ye; Albert Xu; Xiang Ren 001; Robin Jia (EMNLP (Findings), 2023)

How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench. - Qinyuan Ye; Harvey Yiyun Fu; Xiang Ren 001; Robin Jia (EMNLP (Findings), 2023)

Transformers Learn Higher-Order Optimization Methods for In-Context Learning: A Study with Linear Models. - Deqing Fu; Tian-Qi Chen; Robin Jia; Vatsal Sharan (CoRR, 2023)

Do Localization Methods Actually Localize Memorized Data in LLMs? - Ting-Yun Chang; Jesse Thomason; Robin Jia (CoRR, 2023)

Does VLN Pretraining Work with Nonsensical or Irrelevant Instructions? - Wang Zhu 001; Ishika Singh; Yuan Huang; Robin Jia; Jesse Thomason (CoRR, 2023)

Proving membership in LLM pretraining data via data watermarks. - Johnny Tian-Zheng Wei; Ryan Yixiang Wang; Robin Jia (ACL (Findings), 2024)

Operationalizing Content Moderation "Accuracy" in the Digital Services Act. - Johnny Tian-Zheng Wei; Frederike Zufall; Robin Jia (AIES (1), 2024)

When Parts Are Greater Than Sums: Individual LLM Components Can Outperform Full Models. - Ting-Yun Chang; Jesse Thomason; Robin Jia (EMNLP, 2024)

Efficient End-to-End Visual Document Understanding with Rationale Distillation. - Wang Zhu 001; Alekh Agarwal; Mandar Joshi; Robin Jia; Jesse Thomason; Kristina Toutanova (NAACL-HLT, 2024)

Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two Benchmarks. - Ting-Yun Chang; Jesse Thomason; Robin Jia (NAACL-HLT, 2024)

Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression. - Deqing Fu; Tian-Qi Chen; Robin Jia; Vatsal Sharan (NeurIPS, 2024)

Pre-trained Large Language Models Use Fourier Features to Compute Addition. - Tianyi Zhou 011; Deqing Fu; Vatsal Sharan; Robin Jia (NeurIPS, 2024)

IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations. - Deqing Fu; Ghazal Khalighinejad; Ollie Liu; Bhuwan Dhingra; Dani Yogatama; Robin Jia; Willie Neiswanger (CoRR, 2024)

Language Models can Infer Action Semantics for Classical Planners from Environment Feedback. - Wang Zhu 001; Ishika Singh; Robin Jia; Jesse Thomason (CoRR, 2024)

Rethinking Backdoor Detection Evaluation for Language Models. - Jun Yan 012; Wenjie Mo 001; Xiang Ren 001; Robin Jia (CoRR, 2024)

Robust Data Watermarking in Language Models by Injecting Fictitious Knowledge. - Xinyue Cui; Johnny Tian-Zheng Wei; Swabha Swayamdipta; Robin Jia (ACL (Findings), 2025)

Verify with Caution: The Pitfalls of Relying on Imperfect Factuality Metrics. - Ameya Godbole; Robin Jia (ACL (Findings), 2025)

Mechanistic Interpretability of Emotion Inference in Large Language Models. - Ala N. Tak; Amin Banayeeanzade; Anahita Bolourani; Mina Kian; Robin Jia; Jonathan Gratch (ACL (Findings), 2025)

Interrogating LLM design under copyright law. - Johnny Tian-Zheng Wei; Maggie Wang; Ameya Godbole; Jonathan H. Choi; Robin Jia (FAccT, 2025)

TLDR: Token-Level Detective Reward Model for Large Vision Language Models. - Deqing Fu; Tong Xiao; Rui Wang; Wang Zhu 001; Pengchuan Zhang; Guan Pang; Robin Jia; Lawrence Chen 002 (ICLR, 2025)

Language Models Can Infer Action Semantics for Symbolic Planners from Environment Feedback. - Wang Bill Zhu; Ishika Singh; Robin Jia; Jesse Thomason (NAACL (Long Papers), 2025)

FoNE: Precise Single-Token Number Embeddings via Fourier Features. - Tianyi Zhou 011; Deqing Fu; Mahdi Soltanolkotabi; Robin Jia; Vatsal Sharan (CoRR, 2025)

Interrogating LLM design under a fair learning doctrine. - Johnny Tian-Zheng Wei; Maggie Wang; Ameya Godbole; Jonathan H. Choi; Robin Jia (CoRR, 2025)

Promote, Suppress, Iterate: How Language Models Answer One-to-Many Factual Queries. - Tianyi Lorena Yan; Robin Jia (CoRR, 2025)

Cancer-Myth: Evaluating AI Chatbot on Patient Questions with False Presuppositions. - Wang Bill Zhu; Tianqi Chen; Ching Ying Lin; Jade Law; Mazen Jizzini; Jorge J. Nieva; Ruishan Liu; Robin Jia (CoRR, 2025)

Teaching Models to Understand (but not Generate) High-risk Data. - Ryan Wang; Matthew Finlayson; Luca Soldaini; Swabha Swayamdipta; Robin Jia (CoRR, 2025)

Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models. - Woody Haosheng Gan; Deqing Fu; Julian Asilis; Ollie Liu; Dani Yogatama; Vatsal Sharan; Robin Jia; Willie Neiswanger (CoRR, 2025)

When Do LLMs Admit Their Mistakes? Understanding the Role of Model Belief in Retraction. - Yuqing Yang; Robin Jia (CoRR, 2025)

Why Do Some Inputs Break Low-Bit LLM Quantization? - Ting-Yun Chang; Muru Zhang; Jesse Thomason; Robin Jia (CoRR, 2025)

PSALM-V: Automating Symbolic Planning in Interactive Visual Environments with Large Language Models. - Wang Bill Zhu; Miaosen Chai; Ishika Singh; Robin Jia; Jesse Thomason (CoRR, 2025)

Function Induction and Task Generalization: An Interpretability Study with Off-by-One Addition. - Qinyuan Ye; Robin Jia; Xiang Ren 001 (CoRR, 2025)

Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning. - Ang Li; Charles Wang; Kaiyu Yue; Zikui Cai; Ollie Liu; Deqing Fu; Peng Guo; Wang Bill Zhu; Vatsal Sharan; Robin Jia; Willie Neiswanger; Furong Huang; et al. (CoRR, 2025)

TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability. - Mohammad Aflah Khan; Ameya Godbole; Johnny Tian-Zheng Wei; Ryan Wang; James Flemings; Krishna P. Gummadi; Willie Neiswanger; Robin Jia (CoRR, 2025)

LLM Unlearning Without an Expert Curated Dataset. - Xiaoyuan Zhu; Muru Zhang; Ollie Liu; Robin Jia; Willie Neiswanger (CoRR, 2025)