Choi, Eunsol

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Joshi, M., Choi, E., Weld, D., & Zettlemoyer, L. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 1,690

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Joshi, M., Choi, E., Weld, D. S., & Zettlemoyer, L. (2017) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,650

QuAC: Question Answering in Context

Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.-T., Choi, Y., Liang, P., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Topic ModelingSpeech and dialogue systems

Cited by 714

Zero-Shot Relation Extraction via Reading Comprehension

Levy, O., Seo, M., Choi, E., & Zettlemoyer, L. (2017) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 482

Zero-Shot Relation Extraction via Reading Comprehension

Levy, O., Seo, M., Choi, E., & Zettlemoyer, L. (2017) · Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Topic ModelingNatural Language Processing Techniques

Cited by 482

Coarse-to-Fine Question Answering for Long Documents

Choi, E., Hewlett, D., Uszkoreit, J., Polosukhin, I., Lacoste, A., & Berant, J. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 177

Decontextualization: Making Sentences Stand-Alone

Choi, E., Palomaki, J., Lamm, M., Kwiatkowski, T., Das, D., & Collins, M. (2021) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 54

Generating Literal and Implied Subquestions to Fact-check Complex Claims

Chen, J., Sriram, A., Choi, E., & Durrett, G. (2022) · arXiv

Misinformation and Its ImpactsTopic Modeling

Cited by 34

Generating Literal and Implied Subquestions to Fact-check Complex Claims

Chen, J., Sriram, A., Choi, E., & Durrett, G. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Misinformation and Its ImpactsTopic Modeling

Cited by 34

A Critical Evaluation of Evaluations for Long-form Question Answering

Xu, F., Song, Y., Iyyer, M., & Choi, E. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 33

Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence

Chen, H.-T., Zhang, M., & Choi, E. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Topic ModelingInformation Retrieval and Search Behavior

Cited by 31

Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence

Chen, H.-T., Zhang, M. J. Q., & Choi, E. (2022) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 31

Propagating Knowledge Updates to LMs Through Distillation

Padmanabhan, S., Onoe, Y., Zhang, M., Durrett, G., & Choi, E. (2023) · Advances in Neural Information Processing Systems 36

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 6

CodeUpdateArena: Benchmarking Knowledge Editing on API Updates

Liu, Z. L., Pandit, S., Ye, X., Choi, E., & Durrett, G. (2024) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 6

User Feedback in Human-LLM Dialogues: A Lens to Understand Users But Noisy as a Learning Signal

Liu, Y., Zhang, M. J. Q., & Choi, E. (2025) · arXiv

Speech and dialogue systemsInformation Retrieval and Search Behavior

Cited by 3

KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questions

Xu, F., Lo, K., Soldaini, L., Kuehl, B., Choi, E., & Wadden, D. (2024) · Findings of the Association for Computational Linguistics ACL 2024

Topic ModelingSpeech and dialogue systems

Cited by 3

Understanding Retrieval Augmentation for Long-Form Question Answering

Chen, H.-T., Xu, F., Arora, S., & Choi, E. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 3

From Distributional to Overton Pluralism: Investigating Large Language Model Alignment

Lake, T., Choi, E., & Durrett, G. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 1