Sentiment Analysis for Reinforcement Learning. - Ameet Deshpande; Eve Fleisig (CoRR, 2020)

Mitigating Gender Bias in Machine Translation through Adversarial Learning. - Eve Fleisig; Christiane Fellbaum (CoRR, 2022)

FairPrism: Evaluating Fairness-Related Harms in Text Generation. - Eve Fleisig; Aubrie Amstutz; Chad Atalla; Su Lin Blodgett; Hal Daumé III; Alexandra Olteanu; Emily Sheng; Dan Vann; Hanna M. Wallach (ACL (1), 2023)

When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Tasks. - Eve Fleisig; Rediet Abebe; Dan Klein 001 (EMNLP, 2023)

Incorporating Worker Perspectives into MTurk Annotation Practices for NLP. - Olivia Huang; Eve Fleisig; Dan Klein 001 (EMNLP, 2023)

Centering the Margins: Outlier-Based Identification of Harmed Populations in Toxicity Detection. - Vyoma Raman; Eve Fleisig; Dan Klein 001 (EMNLP, 2023)

Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination. - Eve Fleisig; Genevieve Smith; Madeline Bossi; Ishita Rustagi; Xavier Yin; Dan Klein 001 (EMNLP, 2024)

Accurate and Data-Efficient Toxicity Prediction when Annotators Disagree. - Harbani Jaggi; Kashyap Coimbatore Murali; Eve Fleisig; Erdem Biyik (EMNLP, 2024)

The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels. - Eve Fleisig; Su Lin Blodgett; Dan Klein 001; Zeerak Talat (NAACL-HLT, 2024)

First Tragedy, then Parse: History Repeats Itself in the New Era of Large Language Models. - Naomi Saphra; Eve Fleisig; Kyunghyun Cho; Adam Lopez (NAACL-HLT, 2024)

Ghostbuster: Detecting Text Ghostwritten by Large Language Models. - Vivek Verma; Eve Fleisig; Nicholas Tomlin; Dan Klein 001 (NAACL-HLT, 2024)

Mapping Social Choice Theory to RLHF. - Jessica Dai; Eve Fleisig (CoRR, 2024)

Standard Language Ideology in AI-Generated Language. - Genevieve Smith; Eve Fleisig; Madeline Bossi; Ishita Rustagi; Xavier Yin (CoRR, 2024)

ADVSCORE: A Metric for the Evaluation and Creation of Adversarial Benchmarks. - Yoo Yeon Sung; Eve Fleisig; Ishani Mondal; Jordan Lee Boyd-Graber (CoRR, 2024)

GRACE: A Granular Benchmark for Evaluating Model Calibration against Human Calibration. - Yoo Yeon Sung; Eve Fleisig; Yu Hou; Ishan Upadhyay; Jordan Lee Boyd-Graber (ACL (1), 2025)

Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness. - Yoo Yeon Sung; Maharshi Gor; Eve Fleisig; Ishani Mondal; Jordan Lee Boyd-Graber (NAACL (Long Papers), 2025)

GRACE: A Granular Benchmark for Evaluating Model Calibration against Human Calibration. - Yoo Yeon Sung; Eve Fleisig; Yufang Hou 001; Ishan Upadhyay; Jordan Lee Boyd-Graber (CoRR, 2025)