Papers by Robin Jia

Do Explanations Help Users Detect Errors in Open-Domain QA? An Evaluation of Spoken vs. Visual Explanations (2021.findings-acl)

Challenge:	Dynamic adversarial data collection (DADC) can be used to build models that are robust across a wide range of test inputs.
Approach:	They propose to run Dynamic adversarial data collection over many rounds to maximize its training-time benefits.
Outcome:	The proposed model makes 26% fewer errors on the premise paragraphs compared to models trained on non-adversarial examples.

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Ana Valeria González, Gagan Bansal, Angela Fan, Yashar Mehdad, Robin Jia, Srinivasan Iyer

Explainability & Interpretability in NLP

Challenge:	despite interest in explainable AI, there is increasing skepticism as to whether explanations are useful to end-users in downstream applications.
Approach:	They conduct user studies to measure whether explanations help users decide when to accept or reject an ODQA system's answer.
Outcome:	The proposed study shows that explanations outperform baselines across modalities but the best strategy varies with the modality.

Data Curation Alone Can Stabilize In-context Learning (2023.acl-long)

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Ting-Yun Chang, Robin Jia

On the Robustness of Reading Comprehension Models to Entity Renaming (2022.naacl-main)

Challenge:	In-context learning (ICL) is a new paradigm for few-shot learning with pretrainable large language models . however, randomly sampling examples from a training set leads to high variance in performance .
Approach:	They propose two methods to select training examples from a training set and then carefully curate them from corresponding subsets.
Outcome:	The proposed method improves accuracy over sampling from the entire training set.

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Jun Yan, Yang Xiao, Sagnik Mukherjee, Bill Yuchen Lin, Robin Jia, Xiang Ren

Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little (2021.emnlp-main)

Challenge:	SpanBERT model is more robust than RoBERTa, despite having similar accuracy on unperturbed test data.
Approach:	They propose a pipeline to replace entity names with names from a variety of sources.
Outcome:	The proposed model performs worse when entities are renamed, the authors show . SpanBERT, which is pretrained with span-level masking, is more robust than RoBERTa .

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Koustuv Sinha, Robin Jia, Dieuwke Hupkes, Joelle Pineau, Adina Williams, Douwe Kiela

Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems (2022.findings-emnlp)

Challenge:	masked language models (MLMs) pre-train to model higher-order word co-occurrence statistics . authors suggest that such models have learned to represent syntactic structures prevalent in classical NLP pipelines . purely distributional information largely explains the success of pre-training, authors say .
Approach:	They propose to pre-train masked language models on sentences with random shuffled word order and show they still achieve high accuracy after fine-tuning on many downstream tasks.
Outcome:	The proposed model performs well according to parametric syntactic probes . the authors argue that the model is not all that different from earlier distributional models .

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Wang Zhu, Jesse Thomason, Robin Jia

Multimodality

Challenge:	Widely used multi-modal pretrained models generalize poorly to out-of-distribution data, suggesting shortcomings in the VLE2E pipeline.
Approach:	They develop a segment-combine test for multi-image queries and contrast set for cross-benchmark transfer.
Outcome:	The proposed method shows that it is possible to train both neural and neuro-symbolic models in the same way.

Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering (2023.emnlp-main)

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Wang Zhu, Jesse Thomason, Robin Jia

On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks (2020.findings-emnlp)

Challenge:	Existing methods for multistep question answering have shown promise in generating multistep solutions, but they lack robustness.
Approach:	They propose a framework that trains a model to robustly answer multistep questions by generating and answering sub-questions.
Outcome:	The proposed framework outperforms neuro-symbolic methods on a DROP contrast set and GPT-3.5 on QA adversarial sets.

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Stephen Mussmann, Robin Jia, Percy Liang

Low-Resource NLP

Challenge:	Recent datasets heuristically choose examples to ensure label balance . state-of-the-art models trained on QQP and WikiQA have only 2.4% average precision .
Approach:	They show that recent datasets heuristically choose examples to ensure label balance . they instead use active learning to retrieve uncertain points from a large pool of unlabeled utterance pairs .
Outcome:	The proposed model improves on QQP and WikiQA by using more informative negative examples.

SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples (2023.emnlp-main)

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Deqing Fu, Ameya Godbole, Robin Jia

Challenge:	Manually collecting challenging negative examples can be costly and domain-specific.
Approach:	They propose a method that synthesizes negative examples from only positive ones using a mask infilling model.
Outcome:	The proposed method closes 69.6% of the performance gap on a dataset where half of the evaluation examples are unanswerable compared to a model trained on SQuAD 2.0.

When Parts Are Greater Than Sums: Individual LLM Components Can Outperform Full Models (2024.emnlp-main)

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Ting-Yun Chang, Jesse Thomason, Robin Jia

Challenge:	In-context learning is a method for adapting large language models to tasks with instructions or examples.
Approach:	They propose a method to decompose the output of large language models into components . they propose component reweighting, which learns to linearly re-scale component activations from a few labeled examples.
Outcome:	The proposed method improves by 6.0% accuracy points over 24 examples given 24 examples on Llama-2-7B.

Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation (2021.emnlp-main)

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Max Bartolo, Tristan Thrush, Robin Jia, Sebastian Riedel, Pontus Stenetorp, Douwe Kiela

Dynabench: Rethinking Benchmarking in NLP (2021.naacl-main)

Challenge:	a new approach to generate adversarial data is needed to improve question answering models . crowdworkers can fool a model only 8.8% of the time, compared to 17.6% for a trained model without synthetic data.
Approach:	They develop a pipeline that generates questions and then filters or labels them to improve quality.
Outcome:	The proposed approach improves state-of-the-art on a human-written adversarial dataset by 3.7F1 and improves model generalisation on nine of the twelve MRQA datasets.

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Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, Zhiyi Ma, Tristan Thrush, Sebastian Riedel, Zeerak Waseem, Pontus Stenetorp, Robin Jia, Mohit Bansal, Christopher Potts, Adina Williams

Challenge:	Dynabench is an open-source platform for dynamic dataset creation and model benchmarking.
Approach:	They propose an open-source platform for dynamic dataset creation and model benchmarking.
Outcome:	The proposed platform can be used to create models that fail on simple challenges and falter in real-world scenarios.

Mechanistic Interpretability of Emotion Inference in Large Language Models (2025.findings-acl)

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Ala N. Tak, Amin Banayeeanzade, Anahita Bolourani, Mina Kian, Robin Jia, Jonathan Gratch

Proving membership in LLM pretraining data via data watermarks (2024.findings-acl)

Challenge:	Existing studies on large language models (LLMs) show promising capabilities in predicting human emotions from text.
Approach:	They investigate how autoregressive LLMs infer emotions by focusing on appraisal theory . they show that emotion representations are functionally localized to specific regions in the model .
Outcome:	The proposed model is functionally localized to specific regions in the model, and the results align with theoretical and intuitive expectations.

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Johnny Wei, Ryan Wang, Robin Jia

Do Question Answering Modeling Improvements Hold Across Benchmarks? (2023.acl-long)

Challenge:	Detecting whether copyright holders’ works were used in large language model (LLM) training is poised to be an important problem.
Approach:	They propose to use data watermarks to enable principled detection with only black-box model access, provided the rightholder contributed multiple training documents and watermarked them before public release.
Outcome:	The proposed method can be used to test hypothesis testing on a black-box model . it shows that the watermarks are strong under model and dataset scaling .

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Nelson F. Liu, Tony Lee, Robin Jia, Percy Liang

Natural Language Interfaces

Challenge:	a new study finds that human-constructed and downsampled benchmarks hold more concurrence than downsampled benchmarks.
Approach:	They propose to measure concurrence between two QA benchmarks on a set of 20 models . they find that human-constructed benchmarks have high concurrence amongst themselves .
Outcome:	The proposed models hold broadly across the diverse landscape of question answering (QA) benchmarks.

Estimating Large Language Model Capabilities without Labeled Test Data (2023.findings-emnlp)

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Harvey Fu, Qinyuan Ye, Albert Xu, Xiang Ren, Robin Jia

Know What You Don’t Know: Unanswerable Questions for SQuAD (P18-2)

Challenge:	Large Language Models have shown impressive ability to perform in-context learning from only a few examples, but their accuracy varies widely from task to task.
Approach:	They propose a method that trains a meta-model using LLM confidence scores as features to perform ICL accuracy estimation.
Outcome:	The proposed method improves over baselines across 7 out of 12 settings and achieves the same accuracy as evaluating on 40 sampled examples per task.

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Pranav Rajpurkar, Robin Jia, Percy Liang

Question Answering

Challenge:	Existing datasets focus on answerable questions or use automatically generated unanswerable questions that are easy to identify.
Approach:	They propose a dataset that combines the Stanford Question Answering Dataset with 50,000 unanswerable questions written by crowdworkers to look similar to answerable ones.
Outcome:	The proposed dataset looks similar to answerable questions on crowd-written questions . strong neural system that gets 86% F1 on SQuAD achieves only 66% F1.

MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension (D19-58)

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Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, Danqi Chen

Natural Language Interfaces

Challenge:	MRQA datasets have been used to benchmark progress in general-purpose language understanding.
Approach:	They propose to combine 18 question answering datasets into one shared task to evaluate their generalization capabilities.
Outcome:	The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than baseline based on BERT.

Why Do Some Inputs Break Low-Bit LLM Quantization? (2025.emnlp-main)

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Ting-Yun Chang, Muru Zhang, Jesse Thomason, Robin Jia

Rethinking Backdoor Detection Evaluation for Language Models (2025.emnlp-main)

Challenge:	Low-bit weight-only quantization reduces memory usage but disproportionately affects certain examples.
Approach:	They analyze quantization errors of 50 pairs of methods on large language models and test their hypothesis .
Outcome:	The proposed method reduces the memory footprint of large language models while maintaining reasonable performance across benchmarks.

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Jun Yan, Wenjie Jacky Mo, Xiang Ren, Robin Jia

With Little Power Comes Great Responsibility (2020.emnlp-main)

Challenge:	Existing backdoor detection methods have high accuracy in detecting backdoored models, but they are not robust enough to detect backdoors in the wild.
Approach:	They examine the robustness of backdoor detectors by manipulating different factors during backdoor planting.
Outcome:	The proposed methods are able to detect backdoors in the wild, but they lack robustness against backdoor attacks.

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Dallas Card, Peter Henderson, Urvashi Khandelwal, Robin Jia, Kyle Mahowald, Dan Jurafsky

Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two Benchmarks (2024.naacl-long)

Challenge:	Underpowered experiments make it more difficult to discern the difference between statistical noise and meaningful model improvements and increase the chances of exaggerated findings.
Approach:	They characterize typical statistical power for a variety of settings and characterize it by a set of existing NLP papers and datasets.
Outcome:	The authors characterize typical power for a variety of settings and find it common in the literature.

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Ting-Yun Chang, Jesse Thomason, Robin Jia

Challenge:	Existing studies on the ability of localization methods to pinpoint LLM components for memorized data are lacking.
Approach:	They propose to use a subset of LLM weights to evaluate localization methods . they propose to measure how much dropping out identified neurons deletes a memorized sequence.
Outcome:	The proposed methods show promising localization ability, despite differences in their evaluations.

Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models (2026.acl-long)

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Woody Haosheng Gan, Deqing Fu, Julian Asilis, Ollie Liu, Vatsal Sharan, Robin Jia, Willie Neiswanger

Multimodality

Challenge:	Steering methods have emerged as effective tools for guiding large language models’ behavior, yet multimodal large language model lacks comparable techniques due to architectural diversity and limited availability of multimodal steering vectors.
Approach:	They validate steering vectors derived solely from text-only LLM backbones and use a cross-modal transfer technique to reuse existing interpretability tools.
Outcome:	The proposed steering vectors can guide and enhance multimodal models using SPAR, Mean Shift, and Linear Probing.

The statistical advantage of automatic NLG metrics at the system level (2021.acl-long)

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Johnny Wei, Robin Jia

Text Generation

Challenge:	Statistically, humans are unbiased, high variance estimators, while metrics are biased, low variance estimator.
Approach:	They compare automatic metrics to humans and a derived, perfect segment-level annotator by applying a bias-variance-noise decomposition to adjust the error to a noise-free, infinite test set setting.
Outcome:	The proposed method outperforms humans and a derived, perfect segment-level annotator in two settings.

Document-Level N-ary Relation Extraction with Multiscale Representation Learning (N19-1)

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Robin Jia, Cliff Wong, Hoifung Poon

Information Extraction & Text Mining

Challenge:	Existing work on cross-sentence relation extraction is limited to three consecutive sentences, which severely limits recall.
Approach:	They propose a multiscale neural architecture for document-level n-ary relation extraction that combines representations learned over various text spans throughout the document and across the subrelation hierarchy.
Outcome:	The proposed system outperforms existing methods on biomedical machine reading.

Benchmarking Long-tail Generalization with Likelihood Splits (2023.findings-eacl)

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Ameya Godbole, Robin Jia

Are Sample-Efficient NLP Models More Robust? (2023.acl-short)

Challenge:	Existing methods to test out of distribution generalization have their own strengths and weaknesses.
Approach:	They propose a method to create challenging benchmarks that require generalizing to the tail of the distribution by re-splitting existing datasets.
Outcome:	The proposed approach can be customized to construct meaningful splits for a wide range of tasks.

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Nelson F. Liu, Ananya Kumar, Percy Liang, Robin Jia

Verify with Caution: The Pitfalls of Relying on Imperfect Factuality Metrics (2025.findings-acl)

Challenge:	Recent work in image classification and extractive question answering have observed that pre-trained models trained on less in-distribution data have better out-of-distortion performance.
Approach:	They conduct a large empirical study to investigate the relationship between sample efficiency and robustness.
Outcome:	The results show that pre-trained models with lower sample efficiency perform better on some tasks but not others.

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Ameya Godbole, Robin Jia

Text Generation

Challenge:	Recent advances in large language models have led to optimism that they can serve as reliable evaluators of natural language outputs.
Approach:	They propose to use factuality metrics to evaluate natural language outputs . they find they misestimate the factual accuracy of NLG systems .
Outcome:	The proposed metrics are inconsistent with each other and often misestimate the factual accuracy of NLG systems, causing biases against paraphrased outputs and outputs that draw upon faraway parts of the source documents.

Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants (2022.naacl-main)

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Max Bartolo, Tristan Thrush, Sebastian Riedel, Pontus Stenetorp, Robin Jia, Douwe Kiela

On Continual Model Refinement in Out-of-Distribution Data Streams (2022.acl-long)

Challenge:	Dynamic Adversarial Data Collection (DADC) is a time-consuming and costly approach . DADC is based on training data collected from adversarial and out-of-domain settings .
Approach:	They propose a dynamic data collection approach that uses generator-in-the-loop models to provide real-time suggestions that annotators can approve, modify, or reject.
Outcome:	The proposed model is more robust in adversarial and out-of-domain settings and harder for humans to fool.

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Bill Yuchen Lin, Sida Wang, Xi Lin, Robin Jia, Lin Xiao, Xiang Ren, Scott Yih

Challenge:	Existing continual learning (CL) problems cannot cover real-world scenarios such as out-of-distribution errors.
Approach:	They propose a continual model refinement problem formulation to solve this problem . they extend several existing continual learning approaches to the CMR problem based on a general sampling algorithm .
Outcome:	The proposed model refinement solution improves on existing models and their performance metrics.

Robust Encodings: A Framework for Combating Adversarial Typos (2020.acl-main)

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Erik Jones, Robin Jia, Aditi Raghunathan, Percy Liang

Robust Data Watermarking in Language Models by Injecting Fictitious Knowledge (2025.findings-acl)

Challenge:	Existing procedures to defend against adversarial perturbations of inputs are heuristic in nature and susceptible to stronger attacks.
Approach:	They propose a framework that provides robustness without compromises on model architecture.
Outcome:	The proposed framework achieves robustness on six tasks while incompatible with state-of-the-art models like BERT.

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Xinyue Cui, Johnny Wei, Swabha Swayamdipta, Robin Jia

How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench (2023.findings-emnlp)

Challenge:	Data watermarking in language models injects traceable signals, such as specific token sequences or stylistic patterns, into copyrighted text, allowing copyright holders to track and verify training data ownership.
Approach:	They propose a data watermarking approach that injects coherent and plausible yet fictitious knowledge into training data using generated passages describing a fictious entity and its associated attributes.
Outcome:	The proposed method is designed to be memorized by the LLM, and that increasing their density, length, and diversity of attributes strengthens their memorization.

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Qinyuan Ye, Harvey Fu, Xiang Ren, Robin Jia

TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability (2025.emnlp-demos)

Challenge:	a recent study shows that large language models can be used to predict performance on new configurations.
Approach:	They investigate the predictability of large language model capabilities by using BIG-bench . they find a subset of BIG-Bench tasks as informative as BIG-bnch Hard .
Outcome:	The proposed model achieves an R2 score greater than 95% on BIG-bench . the model is 3 smaller than BIG-Bench Hard, and the model performs better on the full set.

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Mohammad Aflah Khan, Ameya Godbole, Johnny Wei, Ryan Yixiang Wang, James Flemings, Krishna P. Gummadi, Willie Neiswanger, Robin Jia

Robustness and Adversarial Examples in Natural Language Processing (2021.emnlp-tutorials)

Challenge:	Existing workflows for pretraining large language models are cumbersome, fragmented and inaccessible.
Approach:	They propose an open-source library for editing, inspection, and analysis of large language model datasets.
Outcome:	TokenSmith is an open-source library for editing, inspection, and analysis of large language model datasets.

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Kai-Wei Chang, He He, Robin Jia, Sameer Singh

Selective Question Answering under Domain Shift (2020.acl-main)

Challenge:	This tutorial aims to raise awareness of practical concerns about NLP robustness . it aims at addressing the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift .
Approach:	This tutorial aims to bring awareness of practical concerns about NLP robustness . it reviews recent studies on analyzing the weakness of NLP systems when facing adversarial inputs .
Outcome:	This tutorial aims to bring awareness of practical concerns about NLP robustness . it will examine the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift .

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Amita Kamath, Robin Jia, Percy Liang

Natural Language Interfaces

Challenge:	Deployed question answering (QA) models need to know when to abstain from answering questions that diverge from their training data.
Approach:	They propose a selective question answering under domain shift in which a QA model is tested on a mixture of in-domain and out-of-domain data and must answer (i.e., not abstain on) as many questions as possible.
Outcome:	The proposed method answers 56% of questions while maintaining 80% accuracy.

Efficient End-to-End Visual Document Understanding with Rationale Distillation (2024.naacl-long)

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Wang Zhu, Alekh Agarwal, Mandar Joshi, Robin Jia, Jesse Thomason, Kristina Toutanova

Visual Data in NLP

Challenge:	Pre-processing tools such as optical character recognition (OCR) can map document image inputs to textual tokens, then large language models (LLMs) can reason over text.
Approach:	They propose a method that integrates outputs of OCR tools and larger multimodal models as intermediate "rationales" a student model is trained to predict rationales and answers based on visual documents .
Outcome:	The proposed model outperforms the base model on three visual document understanding benchmarks with only 1% higher computational cost.

Question Answering Infused Pre-training of General-Purpose Contextualized Representations (2022.findings-acl)

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Robin Jia, Mike Lewis, Luke Zettlemoyer

Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer (N18-1)

Challenge:	Existing pretraining objectives for question answering (QA) are not optimized for being immediately useful without fine-tuning.
Approach:	They propose a pre-training objective based on question answering (QA) that is based more directly on context.
Outcome:	The proposed model matches predictions of a more accurate cross-encoder model on 80 million synthesized QA pairs and achieves large improvements over previous state-of-the-art models on paraphrase detection and fewshot named entity recognition.

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Juncen Li, Robin Jia, He He, Percy Liang

Sentiment Analysis

Challenge:	Previous work using adversarial methods has struggled to produce high-quality outputs.
Approach:	They propose a method that transforms a sentence to alter a specific attribute while preserving its attribute-independent content.
Outcome:	The proposed method generates grammatical and appropriate responses on 22% more inputs than the best previous system, averaged over three attribute transfer datasets.

Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality (2021.naacl-main)

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Mina Lee, Chris Donahue, Robin Jia, Alexander Iyabor, Percy Liang

Challenge:	Existing benchmarks for lexical substitution depend on human recall as the only source of data, authors say . existing benchmarks lack coverage of the appropriate substitutes that would be most helpful to humans .
Approach:	They propose a benchmark for lexical substitution to find appropriate substitutes for a target word in context . existing benchmarks depend on human recall as the only source of data, they argue .
Outcome:	The new benchmark for lexical substitution uses a context-free thesaurus . it has 3x as many substitutes per target word for the same quality, and substitutes are 1.4x more appropriate .

Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language Models (2023.acl-long)

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Albert Xu, Xiang Ren, Robin Jia

Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards? (2021.acl-long)

Challenge:	Existing methods for classification are overly confident on unseen examples . despite recent advances in NLP, some categories of distribution shift still pose serious challenges.
Approach:	They propose a method that generates OOD examples representative of novel classes and trains to decrease confidence on them.
Outcome:	The proposed method improves classifiers' ability to detect and abstain on novel class examples over previous methods by 2.3% and 5.5% over previous approaches.

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Pedro Rodriguez, Joe Barrow, Alexander Miserlis Hoyle, John P. Lalor, Robin Jia, Jordan Boyd-Graber

Certified Robustness to Adversarial Word Substitutions (D19-1)

Challenge:	Rather than replacing leaderboards, we advocate a re-imagining of the model to highlight if and where progress is made.
Approach:	They propose a Bayesian leaderboard model where latent subject skill and latent item difficulty predict correct responses.
Outcome:	The proposed model can guide what to annotate, identify annotation errors, detect overfitting, and identify informative examples.

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Robin Jia, Aditi Raghunathan, Kerem Göksel, Percy Liang

Language Models Can Infer Action Semantics for Symbolic Planners from Environment Feedback (2025.naacl-long)

Challenge:	State-of-the-art NLP models can be fooled by adversaries that apply seemingly innocuous label-preserving transformations to input text.
Approach:	They propose to train models that are provably robust to all word substitutions in a family of label-preserving transformations that can be replaced with a similar word without changing the original sentiment.
Outcome:	The proposed models achieve 75% adversarial accuracy on both sentiment analysis and natural language inference on IMDB and SNLI compared to models trained normally and ones trained with data augmentation.

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Wang Bill Zhu, Ishika Singh, Robin Jia, Jesse Thomason