Choi, Yejin

Agent AI: Surveying the Horizons of Multimodal Interaction

Durante, Z., Huang, Q., Wake, N., Gong, R., Park, J. S., Sarkar, B., Taori, R., Noda, Y., Terzopoulos, D., Choi, Y., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsSpeech and dialogue systems

Cited by 75

GO FIGURE: A Meta Evaluation of Factuality in Summarization

Gabriel, S., Celikyilmaz, A., Jha, R., Choi, Y., & Gao, J. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Topic ModelingNatural Language Processing Techniques

Cited by 75

Scruples: A Corpus of Community Ethical Judgments on 32, 000 Real-Life Anecdotes

Lourie, N., Le Bras, R., & Choi, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence

Ethics and Social Impacts of AIEthics in Business and Education

Cited by 73

SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization

Kim, H., Hessel, J., Jiang, L., West, P., Lu, X., Yu, Y., Zhou, P., Bras, R., Alikhani, M., Kim, G., et al. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Speech and dialogue systemsTopic Modeling

Cited by 63

Do Neural Language Representations Learn Physical Commonsense?

Forbes, M., Holtzman, A., & Choi, Y. (2019) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 60

TIMEDIAL: Temporal Commonsense Reasoning in Dialog

Qin, L., Gupta, A., Upadhyay, S., He, L., Choi, Y., & Faruqui, M. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 59

Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization

Ramamurthy, R., Ammanabrolu, P., Brantley, K., Hessel, J., Sifa, R., Bauckhage, C., Hajishirzi, H., & Choi, Y. (2022) · arXiv

Reinforcement Learning in RoboticsNatural Language Processing Techniques

Cited by 56

Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting

Sclar, M., Choi, Y., Tsvetkov, Y., & Suhr, A. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 52

Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation

Huang, K.-H., McKeown, K., Nakov, P., Choi, Y., & Ji, H. (2022) · arXiv

Misinformation and Its ImpactsSpam and Phishing Detection

Cited by 46

Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models

Shapira, N., Levy, M., Alavi, S. H., Zhou, X., Choi, Y., Goldberg, Y., Sap, M., & Shwartz, V. (2024) · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

Child and Animal Learning DevelopmentTopic Modeling

Cited by 44

Generating Sequences by Learning to Self-Correct

Welleck, S., Lu, X., West, P., Brahman, F., Shen, T., Khashabi, D., & Choi, Y. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 41

PIQA: Reasoning about Physical Commonsense in Natural Language

Bisk, Y., Zellers, R., Le bras, R., Gao, J., & Choi, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Topic ModelingMultimodal Machine Learning Applications

Cited by 37

FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions

Kim, H., Sclar, M., Zhou, X., Bras, R., Kim, G., Choi, Y., & Sap, M. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Child and Animal Learning DevelopmentTopic Modeling

Cited by 27

Minding Language Models’ (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker

Sclar, M., Kumar, S., West, P., Suhr, A., Choi, Y., & Tsvetkov, Y. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Child and Animal Learning DevelopmentLogic, Reasoning, and Knowledge

Cited by 27

Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration

Feng, S., Sorensen, T., Liu, Y., Fisher, J., Park, C. Y., Choi, Y., & Tsvetkov, Y. (2024) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 18

Do Membership Inference Attacks Work on Large Language Models?

Duan, M., Suri, A., Mireshghallah, N., Min, S., Shi, W., Zettlemoyer, L., Tsvetkov, Y., Choi, Y., Evans, D., & Hajishirzi, H. (2024) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 17

Agent Lumos: Unified and Modular Training for Open-Source Language Agents

Yin, D., Brahman, F., Ravichander, A., Chandu, K., Chang, K.-W., Choi, Y., & Lin, B. Y. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Natural Language Processing TechniquesSpeech and dialogue systems

Cited by 17

Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory

Mireshghallah, N., Kim, H., Zhou, X., Tsvetkov, Y., Sap, M., Shokri, R., & Choi, Y. (2023) · arXiv

Privacy-Preserving Technologies in DataPrivacy, Security, and Data Protection

Cited by 14

HALoGEN: Fantastic LLM Hallucinations and Where to Find Them

Ravichander, A., Ghela, S., Wadden, D., & Choi, Y. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 13

RewardBench: Evaluating Reward Models for Language Modeling

Lambert, N., Pyatkin, V., Morrison, J., Miranda, L., Lin, B. Y., Chandu, K., Dziri, N., Kumar, S., Zick, T., Choi, Y., et al. (2025) · Findings of the Association for Computational Linguistics: NAACL 2025

Natural Language Processing TechniquesTopic Modeling

Cited by 13

HALoGEN: Fantastic LLM Hallucinations and Where to Find Them

Ravichander, A., Ghela, S., Wadden, D., & Choi, Y. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 13

Do Embodied Agents Dream of Pixelated Sheep?: Embodied Decision Making using Language Guided World Modelling

Nottingham, K., Ammanabrolu, P., Suhr, A., Choi, Y., Hajishirzi, H., Singh, S., & Fox, R. (2023) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 12

The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning

Lin, B. Y., Ravichander, A., Lu, X., Dziri, N., Sclar, M., Chandu, K., Bhagavatula, C., & Choi, Y. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 11

On-the-Fly Attention Modulation for Neural Generation

Dong, Y., Bhagavatula, C., Lu, X., Hwang, J. D., Bosselut, A., Cheung, J. C. K., & Choi, Y. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Topic ModelingNatural Language Processing Techniques

Cited by 10