[Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias. - Sunnie S. Y. Kim; Sharon Zhang; Nicole Meister; Olga Russakovsky (CoRR, 2021)

Acronym Extraction and Acronym Disambiguation Shared Tasks at the Scientific Document Understanding Workshop 2022. - Amir Pouran Ben Veyseh; Nicole Meister; Franck Dernoncourt; Thien Huu Nguyen (SDU@AAAI, 2022)

MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction. - Amir Pouran Ben Veyseh; Nicole Meister; Seunghyun Yoon 002; Rajiv Jain; Franck Dernoncourt; Thien Huu Nguyen (COLING, 2022)

HIVE: Evaluating the Human Interpretability of Visual Explanations. - Sunnie S. Y. Kim; Nicole Meister; Vikram V. Ramaswamy; Ruth Fong; Olga Russakovsky (ECCV (12), 2022)

ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features. - Vikram V. Ramaswamy; Sunnie S. Y. Kim; Nicole Meister; Ruth Fong; Olga Russakovsky (CoRR, 2022)

Improving Keyphrase Extraction with Data Augmentation and Information Filtering. - Amir Pouran Ben Veyseh; Nicole Meister; Franck Dernoncourt; Thien Huu Nguyen (CoRR, 2022)

Gender Artifacts in Visual Datasets. - Nicole Meister; Dora Zhao; Angelina Wang; Vikram V. Ramaswamy; Ruth Fong; Olga Russakovsky (ICCV, 2023)

Proving Test Set Contamination in Black-Box Language Models. - Yonatan Oren; Nicole Meister; Niladri S. Chatterji; Faisal Ladhak; Tatsunori Hashimoto (ICLR, 2024)

Unifying Corroborative and Contributive Attributions in Large Language Models. - Theodora Worledge; Judy Hanwen Shen; Nicole Meister; Caleb Winston; Carlos Guestrin (SaTML, 2024)

Benchmarking Distributional Alignment of Large Language Models. - Nicole Meister; Carlos Guestrin; Tatsunori Hashimoto (NAACL (Long Papers), 2025)