Yejin Choi (original) (raw)
Photo credit: Matt Hagen
| Yejin Choi The Dieter Schwarz Foundation Professor & Senior Fellow MacArthur Fellow Office: Gates 362Phone: 650.725.4537 email: yejinc@stanford.edu | Computer Science & HAI Stanford University 353 Jane Stanford Way Stanford, CA 94305 |
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News:
- I am now with NVIDIA as a senior director 💚
- Starting in 2025, I'll join Stanford as the Dieter Schwarz Foundation Professor of Computer Science and Senior Fellow at Human Centered Artificial Intelligence
- Named among Time100 Most Influential People in AI
- Podcast "Unconfuse Me" with Bill Gates: Full episode here (audio-only) and Youtube highlights here (videos)
- A TED talk: "Why AI is Incredibly Smart --- and Shockingly Stupid"
-
MacArthur Fellow (class of 2022); 2 min YouTube reel 
→here
- Keynote at ACL: _"2082: An ACL Odyssey: The Dark Matter of Language and Intelligence"_along with a fireside chat on "The Trajectory of ACL and the Next 60 years" and a pre-recorded talk →here
- An invited article, "The Curious Case of Commonsense Intelligence"for the Daedalus's special issue on AI & Society
- A podcast interview with the Gradient on commonsense and morality
- Featured by New Yorker: "Can Computers Learn Common Sense?"
- The TWIML AI Podcast with Sam Charrington on "Why is language the best medium for reasoning?"
- An interview by Dhruv Batra on Humans of AI: Stories, Not Stats
- Featured by NY Times on Delphi: "Can a Machine Learn Morality?"
- Promoted to a full professor as of Apr 2021, the new title effective on Sep 2021
- Endowed with the Brett Helsel Career Development Professorship (2020 - 2023)
- Won the AAAI Outstanding Paper Award 2020
- Featured by Quanta Magazine --- 🤖_"Common Sense Comes Closer to Computers"_🤖
- Our UW Sounding Board team is the winnner of the Alexa Prize!
- Our UW team (with Pooja, Max, Ari) won the Facebook ParlAI award!
Awards:
Best/Outstanding Paper Awards:
- Outstanding Paper Award at EMNLP 2023
- Best Paper Award at ACL 2023
- Outstanding Paper Award at ACL 2023
- Outstanding Paper Award at ICML 2022
- Best Paper Award at NAACL 2022
- Outstanding Paper Award at NeurIPS 2021
- Outstanding Paper Award at AAAI 2020
- Marr Prize at ICCV 2013
Test of Time:
- Longuet Higgins Prize at CVPR 2021
- Test of Time Award at ACL 2021
Finalists:
- IROS RoboCup Best Paper Finalist at IROS 2019
- Best Paper Nomination at ACL 2019
Recognition:
- Distinguished Research Fellow at the Institute for Ethics in AI at Oxford (2023)
- Wissner-Slivka Chair (2023 - current)
- MacArthur Fellow (2022)
- ACL Fellow (2022)
- Brett Helsel Career Development Professorship (2020 - 2023)
- Borg Early Career Award (BECA) (2018)
- IEEE AI's 10 to Watch (2016)
Recent Talks (2018 - 2024):
| Oct 2024 | Talk at the the Simons Institute Workshop: Alignment, Trust, Watermarking, and Copyright Issues in LLMs --- due to a scheduling challenge, Taylor Sorensen will present instead |
|---|---|
| Sep 2024 | Talk at the Simons Institute Workshop: Special Year on Large Language Models and Transformers, Part 1 Boot Camp |
| Jun 2024 | Keynote at the DataBricks' Data+AI Summit |
| May 2024 | Keynote at MLSys |
| May 2024 | Keynote at the ICLR Workshop on How Far Are We From AGI? |
| Mar 2024 | Keynote at the Weinberg Cognitive Science Symposium |
| Mar 2024 | Colloquium at Harvard Psychology |
| Dec 2023 | Organizing a NeurIPS Workshop -- 🐌 AI Meets Moral Philosophy and Moral Psychology 🦋 |
| Dec 2023 | Talk at the NeurIPS Workshop -- Socially Responsible Language Modelling Research (SoLaR) |
| Nov 2023 | Talk at UC Irvine |
| Nov 2023 | Talk at UCSD |
| Nov 2023 | Talk at MIT EECS |
| Nov 2023 | Colloquium at MIT Brain and Cognitive Sciences (BCS) |
| Nov 2023 | Distinguished Lecture at UBC |
| Oct 2023 | Talk at the MacArthuer Fellows Forum |
| Oct 2023 | Distinguished Lecture at TTIC |
| Oct 2023 | Talk at the Ohio State University |
| Sep 2023 | Talk at the Department of Philosophy at NYU |
| Sep 2023 | Talk at Cornell Tech |
| Sep 2023 | Talk at MSR @ NYC |
| Aug 2023 | Keynote at VLDB |
| Aug 2023 | Talk at the Simons Institute Workshop on LLMswith the recorded talk →here |
| Aug 2023 | Talk at Duolingo |
| Jun 2023 | Keynote at CVPR |
| Jun 2023 | Talk at the Institute for Ethics in AI at Oxford with the recorded talk →here |
| May 2023 | Keynote at the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) |
| May 2023 | Talk at MPI Tubingen |
| May 2023 | Talk at the Caltech Class on Large Language and Vision Models |
| May 2023 | Talk at the Institute for Assured Autonomy at Johns Hopkins University |
| May 2023 | Talk at Harvard ML Foundations Seminar Series |
| May 2023 | SAIL Distinguished Lecture at Stanford |
| Apr 2023 | Talk at TED with the recorded talk →here |
| Mar 2023 | Talk at AI For Tigray --- cancelled |
| Mar 2023 | Colloquium at Berkeley |
| Mar 2023 | Guest Lecture at the Stanford MLSys seminar co listed with Stanford CS324A: Advances in Foundation Models |
| Feb 2023 | Guest Lecture at the Stanford CS25: Transformers United V2 |
| Feb 2023 | Talk at the Continual Causality AAAI bridge program |
| Jan 2023 | Talk at IBM Workshop on Neuro-Symbolic AI |
| Jan 2023 | Talk at ALPS, the NLP winter school |
| Dec 2022 | Panel at AI Debate 3with the recorded talk →here |
| Dec 2022 | Talk at the NeurIPS workshop on Robustness in Sequence Modeling |
| Nov 2022 | Talk at SoCal NLP Symposium with the recorded talk →here |
| Nov 2022 | Talk at AI2050 |
| Oct 2022 | Colloquium at Hebrew University |
| Oct 2022 | YanDex Distinguished Lecture at Tel-Aviv University |
| Oct 2022 | Panel at Microsoft Research Summit on What’s next in large-scale AI |
| Oct 2022 | Tutorial at COLING on NS4NLP: Neuro-Symbolic Modeling for NLP |
| Sep 2022 | Keynote at Interspeech with the recorded talk →here |
| Sep 2022 | Talk at the Simons Foundation Symposium on New Directions in Theoretical Machine Learning |
| Sep 2022 | Talk at Penn State University |
| Sep 2022 | Keynote at the 19th International Conference on Principles of Knowledge Representation and Reasoning (KR 2022) |
| July 2022 | Keynote at the NAACL Workshop on Narrative Understanding |
| May 2022 | Keynote ACL "2082: An ACL Odyssey: The Dark Matter of Language and Intelligence" and a fireside chat on "The Trajectory of ACL and the Next 60 years" with the pre-recorded talk →here |
| May 2022 | Keynote at the ACL Workshop on Deep Learning Inside Out (DeeLIO): Knowledge Extraction and Integration for Deep Learning Architectures |
| Apr 2022 | Panel on "Ethics, Safety, and AI: Can we have it all?" at the Center for Advancing Safety of Machine Intelligence |
| Mar 2022 | Distinguished Lecture at Microsoft Research |
| Mar 2022 | Keynote at the AAAI Spring Symposium on Ethical Computing: Metrics for Measuring AI’s Proficiency and Competency for Ethical Reasoning |
| Mar 2022 | Distinguished Lecture at Max Planck Institute |
| Feb 2022 | Keynote at WSDM |
| Dec 2021 | Keynote at the NeurIPS workshop on Efficient Natural Language and Speech Processing (ENLSP) |
| Dec 2021 | Keynote at the NeurIPS workshop on CtrlGen: Controllable Generative Modeling in Language and Vision |
| Nov 2021 | Talk at the SNU AI Policy Initiative, SAPI |
| Nov 2021 | Talk at the University of Texas Austin |
| Nov 2021 | Keynote at the EMNLP workshop on Novel Ideas in Learning-to-Learn through Interaction (NILLI) |
| Oct 2021 | Panel at Microsoft Research Summit on Large-scale Neural Platform Models: Opportunities, Concerns, and Directions |
| Oct 2021 | Colloquium at UPenn |
| Oct 2021 | Talk at Georgia Tech: "Knowledge is Power: Symbolic Knowledge Distillation, Commonsense Morality, and Multimodal Script Knowledge" |
| Aug 2021 | Keynote at the Stanford FORUM Workshop on Universal Models: _"David V.S. Goliath: the Art of Leaderboarding in the Era of Extreme-Scale Neural Models"_with the recorded talk →hereand the panel →here |
| Aug 2021 | Panel at the ACL workshop on NLP for Positive Impact |
| Jul 2021 | Distinguished Lecture at Sony |
| Jun 2021 | Keynote at the NAACL workshop on Visually Grounded Interaction and Language (ViGIL) |
| Jun 2021 | Keynote at the NAACL workshop on Advances in Language and Vision Research (ALVR) |
| May 2021 | Keynote at the ICLR workshop on Workshop on Enormous Language Models (WELM): "David V.S. Goliath in the Era of Gigantic Neural Networks" |
| May 2021 | Keynote at ICLR: "Commonsense AI: Myth and Truth" |
| Apr 2021 | Keynote at WebConf (formerly WWW) |
| Mar 2021 | Talk at Facebook AI |
| Feb 2021 | Keynote at the 1st ELLIS NLP Symposium with a recorded talk →here |
| Feb 2021 | Keynote at the AAAI Workshop on Commonsense Knowledge Graphs |
| Jan 2021 | Teaching at the ALPS Winter School with recorded lectures →here |
| Jan 2021 | Talk at UCL Dark Lab with a recorded talk →here |
| Dec 2020 | Panel at AI Debate 2 at Montreal.ai with a recorded talk →here (my 3 min pitch) and →here (the whole 3.5 hr debate) |
| Dec 2020 | Keynote at the Neurips Workshop on Self-Supervised Learning -- Theory and Practice |
| Dec 2020 | Keynote at the Neurips Workshop on Dataset Curation and Security |
| Dec 2020 | Talk at ByteDance |
| Nov 2020 | Keynote at the AAAI Fall Symposium on Abstraction and Analogy |
| Nov 2020 | Talk at the Distinguished Lecture Series at NSF CISE on "Intuitive Reasoning as (Un)supervised Neural Generation" |
| Nov 2020 | Talk at MIT CSAIL Embodied Intelligencewith a recorded talk →here |
| Oct 2020 | Keynote at the Fall Stanford Human-Centered AI (HAI) Conference on Triangulating Intelligence: Melding Neuroscience, Psychology, and AI with a recorded talk →here (from 28:00 to 53:00) |
| Oct 2020 | Distinguished Lecture (Department Colloquium) at Columbia University |
| Oct 2020 | Colloquium at CMU LTI with a recorded talk →here |
| Oct 2020 | Colloquium at University of Maryland CLIP |
| Oct 2020 | Keynote at the Amazon ML Conference 2020 |
| Oct 2020 | Talk at Amazon AI |
| Sep 2020 | Talk at SNU Data Science |
| Aug 2020 | Keynote at the ECCV Workshop on Video Turing Test (VTT): Toward Human-Level Video Story Understandingwith a pre-recorded talk →here |
| Aug 2020 | Keynote at CIFAR Deep Learning Seminar Summer Schoolwith a pre-recorded talk →here |
| Jul 2020 | ACL Tutorial on Commonsensewith a recorded talk →here |
| Jul 2020 | Keynote at the ACL Workshop on Multimodal Language |
| Jul 2020 | Keynote at the ACL Workshop on Fact Extraction and Verification (FEVER)with a recorded talk →here |
| Jun 2020 | CVPR tutorial on Neuro-Symbolic Visual Reasoning and Program Synthesiswith a recorded talk →here |
| Jun 2020 | Keynote at the Workshop on Symbolic-Neural Learning (SNL-2020) --- cancelled until 2021 |
| Apr 2020 | Panel at the ICLR Workshop on Bridging Cognitive Science and AI (BAICS) |
| Feb 2020 | Keynote at the AAAI Workshop on Statistical Relational AI (StarAI) |
| Dec 2019 | Keynote at the Neurips Workshop on KR2ML - Knowledge Representation and Reasoning Meets Machine Learning |
| Dec 2019 | Keynote at the Neurips Workshop on Learning with Rich Experience: Integration of Learning Paradigms |
| Nov 2019 | Keynote at the Natural Language, Dialog and Speech (NDS) Symposium @ the New York Academy of Sciences |
| Nov 2019 | Keynote at the EMNLP Workshop on COmmonsense INference in NLP (COIN) |
| Nov 2019 | Keynote at the ICCV Workshop on Extreme Vision Modeling |
| Nov 2019 | Keynote at the ICCV Workshop on Scene Graph Representation and Learning |
| Nov 2019 | Keynote at the ICCV Workshop on Closing the Loop Between Vision and Language (CLVL) |
| Oct 2019 | Talk at UIUC + TTIC + Northwestern |
| Aug 2019 | Keynote at the ACL Workshop on Conversational AI (ConvAI): "The Curious Case of Neural Conversation Degeneration" |
| July 2019 | Panel at MSR Faculty Summit on the Future of Work + Keynote at KCCV |
| Jun 2019 | Keynote at the NAACL Workshop onNeuralGen :"The Enigma of Neural Text Degeneration as the First Defense to Neural Fake News." |
| May 2019 | Keynote at AKBC + Elemental Cognition + IBM Research: "From Atomic to Comet: Commonsense AKBC." |
| July 2018 | Keynote at the ACL Workshop on Representation Learning for NLP |
| July 2018 | Keynote at LxMLS Lisbon Machine Learning School |
| Jun 2018 | Keynote at the CVPR Workshop on Visual Question Answering |
| Jun 2018 | Keynote at the NAACL Workshop on Generalization in Deep Learning: ``Why NLU Doesn't Generalize Well to NLG.'' |
| Apr 2018 | Keynote at NW-NLP: recorded talk →here |
| Feb 2018 | Keynote at AAAI |
Research Interests:
My primary research interests are in the fields of Natural Language Processing, Machine Learning, Artificial Intelligence, with broader interests in Computer Vision and Digital Humanities.
**Language and X ∈ {vision, knowledge, world, mind, society...} :**Intelligent communication requires the ability to read between the lines and to reason beyond what is said explicitly. My recent research has been under two broad themes: (i) learning the contextual, grounded meaning of language from various contexts in which language is used — both physical (e.g., visual) and abstract (e.g., social, cognitive), and (ii) learning the background knowledge about how the world works, latent in large-scale multimodal data. More specifically, my research interests include:
- **Language Grounding with Vision:**Learning semantic correspondences between language and vision at a very large scale, addressing tasks such as image captioning, multimodal knowledge learning, and reasoning.
- **Physical Commonsense Reasoning:**Learning naive physics type knowledge from language and other modalities; modeling action causality and entailment using frame semantic style representation.
- Social Commonsense Reasoning and Connotation Frames: Modeling connotative implications of actions and events; modeling why people do (intent) what they do and the (emotional) causal impact of different actions and events.
- **Language Generation and Conversational AI:**Modeling the long-term context; tracking and simulating the world representend in a story or a narrative; learning to write; integrating physical and social commonsense in storytelling
- AI for Social Good: fake review / news detection; political factchecking; identifying unwanted bias in modern films and literature
Recent Preprints:
Biased AI can Influence Political Decision-Making
Jillian Fisher, Shangbin Feng, Robert Aron, Thomas Richardson, Yejin Choi, Daniel W. Fisher, Jennifer Pan, Yulia Tsvetkov, Katharina Reinecke
arXiv:2410.06415 WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries
Wenting Zhao, Tanya Goyal, Yu Ying Chiu, Liwei Jiang, Benjamin Newman, Abhilasha Ravichander, Khyathi Chandu, Ronan Le Bras, Claire Cardie, Yuntian Deng, Yejin Choi
arXiv:2407.17468 From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step
Yuntian Deng, Yejin Choi, Stuart Shieber
arXiv:2405.14838 Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging
Joel Jang, Seungone Kim, Bill Yuchen Lin, Yizhong Wang, Jack Hessel, Luke Zettlemoyer, Hannaneh Hajishirzi, Yejin Choi, Prithviraj Ammanabrolu
arXiv:2310.11564
Recent Publications (2016 - 2025):
Position: Political Neutrality in AI is Impossible— But Here’s How to Approximate It
Jillian Fisher, Ruth Elisabeth Appel, Chan Young Park, Yujin Potter, Liwei Jiang, Taylor Sorensen, Shangbin Feng, Yulia Tsvetkov, Margaret Roberts, Jennifer Pan, Dawn Song, Yejin Choi
ICML 2025, Position Track SafetyAnalyst: Interpretable, transparent, and steerable safety moderation for AI behavior
Jing-Jing Li, Valentina Pyatkin, Max Kleiman-Weiner, Liwei Jiang, Nouha Dziri, Anne Collins, Jana Schaich Borg, Maarten Sap, Yejin Choi, Sydney Levine
ICML 2025 Diverging Preferences: When do Annotators Disagree and do Models Know?
Michael JQ Zhang, Zhilin Wang, Jena D. Hwang, Yi Dong, Olivier Delalleau, Yejin Choi, Eunsol Choi, Xiang Ren, Valentina Pyatkin
ICML 2025 ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning
Bill Yuchen Lin, Ronan Le Bras, Kyle Richardson, Ashish Sabharwal, Radha Poovendran, Peter Clark, Yejin Choi
ICML 2025 Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence
Shangbin Feng, Zifeng Wang, Yike Wang, Sayna Ebrahimi, Hamid Palangi, Lesly Miculicich, Achin Kulshrestha, Nathalie Rauschmayr, Yejin Choi, Yulia Tsvetkov, Chen-Yu Lee, Tomas Pfister
ICML 2025 Investigating machine moral judgement through the Delphi experiment
Liwei Jiang, Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny T. Liang, Sydney Levine, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jack Hessel, Jon Borchardt, Taylor Sorensen, Saadia Gabriel, Yulia Tsvetkov, Oren Etzioni, Maarten Sap, Regina Rini and Yejin Choi
Nature Machine Intelligence 2025 Multi-Attribute Constraint Satisfaction via Language Model Rewriting
Ashutosh Baheti, Debanjana Chakraborty, Faeze Brahman, Ronan Le Bras, Ximing Lu, Nouha Dziri, Yejin Choi, Mark Riedl, Maarten Sap
Transactions on Machine Learning Research 2025 LLMs' Potential Influences on Our Democracy: Challenges and Opportunities
Yujin Potter, David Rand, Yejin Choi, Dawn Song
ICLR 2025 Blogpost track Synthetic Visual Genome
Jae Sung Park, Zixian Ma, Linjie Li, Chenhao Zheng, Cheng-Yu Hsieh, Ximing Lu, Khyathi Chandu, Quan Kong, Norimasa Kobori, Ali Farhadi, Yejin Choi, Ranjay Krishna
CVPR 2025 One-Minute Video Generation with Test-Time Training
Jiarui Xu, Shihao Han, Karan Dalal, Daniel Koceja, Yue Zhao, Ka Chun Cheung, Yejin Choi, Jan Kautz, Yu Sun, Xiaolong Wang
CVPR 2025 Language Model Alignment in Multilingual Trolley Problems
Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti, Sydney Levine, Jiarui Liu, Fernando Gonzalez Adauto, Francesco Ortu, András Strausz, Mrinmaya Sachan, Rada Mihalcea, Yejin Choi, Bernhard Schölkopf
ICLR 2025 Spotlight Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement
Jaehun Jung, Faeze Brahman, Yejin Choi
ICLR 2025 Oral AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text
Ximing Lu, Melanie Sclar, Skyler Hallinan, Niloofar Mireshghallah, Jiacheng Liu, Seungju Han, Allyson Ettinger, Liwei Jiang, Khyathi Chandu, Nouha Dziri, Yejin Choi
ICLR 2025 Oral CertainlyUncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness
Khyathi Chandu, Linjie Li, Anas Awadalla, Ximing Lu, Jae Sung Park, Jack Hessel, Lijuan Wang, Yejin Choi
ICLR 2025 Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
Zhangchen Xu, Fengqing Jiang, Luyao Niu, Yuntian Deng, Radha Poovendran, Yejin Choi, Bill Yuchen Lin
ICLR 2025 WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild
Bill Yuchen Lin, Yuntian Deng, Khyathi Chandu, Abhilasha Ravichander, Valentina Pyatkin, Nouha Dziri, Ronan Le Bras, Yejin Choi
ICLR 2025 Spotlight Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset
Yingzi Ma, Jiongxiao Wang, Fei Wang, Siyuan Ma, Jiazhao Li, Jinsheng Pan, Xiujun Li, Furong Huang, Lichao Sun, Bo Li, Yejin Choi, Muhao Chen, Chaowei Xiao
ICLR 2025 Explore Theory of Mind: program-guided adversarial data generation for theory of mind reasoning
Melanie Sclar, Jane Dwivedi-Yu, Maryam Fazel-Zarandi, Yulia Tsvetkov, Yonatan Bisk, Yejin Choi, Asli Celikyilmaz
ICLR 2025 DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of Daily Life
Yu Ying Chiu, Liwei Jiang, Yejin Choi
ICLR 2025 Spotlight Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models
Abhilasha Ravichander, Jillian Fisher, Taylor Sorensen, Ximing Lu, Maria Antoniak, Bill Yuchen Lin, Niloofar Mireshghallah, Chandra Bhagavatula, Yejin Choi
NAACL 2025 Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMs
Aly M. Kassem, Omar Mahmoud, Niloofar Mireshghallah, Hyunwoo Kim, Yulia Tsvetkov, Yejin Choi, Sherif Saad, Santu Rana
NAACL 2025 RewardBench: Evaluating Reward Models for Language Modeling
Nathan Lambert, Valentina Pyatkin, Jacob Morrison, Lester James Validad Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi
NAACL 2025 Findings Foundational Challenges in Assuring Alignment and Safety of Large Language Models
Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, Jose Hernandez-Orallo, Lewis Hammond, Eric Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Aleksandar Petrov, Christian Schroeder de Witt, Sumeet Ramesh Motwan, Yoshua Bengio, Danqi Chen, Philip H.S. Torr, Samuel Albanie, Tegan Maharaj, Jakob Foerster, Florian Tramer, He He, Atoosa Kasirzadeh, Yejin Choi, David Krueger
TMLR 2024 WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models
Liwei Jiang, Kavel Rao, Seungju Han, Allyson Ettinger, Faeze Brahman, Sachin Kumar, Niloofar Mireshghallah, Ximing Lu, Maarten Sap, Nouha Dziri, Yejin Choi
NeurIPS 2024 Data Mixture Inference Attack: BPE Tokenizers Reveal Training Data Compositions
Jonathan Hayase, Alisa Liu, Yejin Choi, Sewoong Oh, Noah A. Smith
NeurIPS 2024 Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback
Hamish Ivison, Yizhong Wang, Jiacheng Liu, Zeqiu Wu, Valentina Pyatkin, Nathan Lambert, Noah A. Smith, Yejin Choi, Hannaneh Hajishirzi
NeurIPS 2024 WildVision: Evaluating Vision-Language Models in the Wild with Human Preferences
Yujie Lu, Dongfu Jiang, Wenhu Chen, William Yang Wang, Yejin Choi, Bill Yuchen Lin
NeurIPS 2024, Datasets and Benchmarks Track ActionAtlas: A VideoQA Benchmark for Fine-grained Action Recognition
Mohammadreza Salehi, Jae Sung Park, Aditya Kusupati, Ranjay Krishna, Yejin Choi, Hannaneh Hajishirzi, Ali Farhadi
NeurIPS 2024, Datasets and Benchmarks Track WildGuard: Open One-stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs
Seungju Han, Kavel Rao, Allyson Ettinger, Liwei Jiang, Bill Yuchen Lin, Nathan Lambert, Nouha Dziri, Yejin Choi
NeurIPS 2024, Datasets and Benchmarks Track The Art of Saying No: Contextual Noncompliance in Language Models
Faeze Brahman, Sachin Kumar, Vidhisha Balachandran, Pradeep Dasigi, Valentina Pyatkin, Abhilasha Ravichander, Sarah Wiegreffe, Nouha Dziri, Khyathi Chandu, Jack Hessel, Yulia Tsvetkov, Noah A. Smith, Yejin Choi, Hannaneh Hajishirzi
NeurIPS 2024, Datasets and Benchmarks Track MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
Anas Awadalla, Le Xue, Oscar Lo, Manli Shu, Hannah Lee, Etash Kumar Guha, Sheng Shen, Mohamed Awadalla, Silvio Savarese, Caiming Xiong, Ran Xu, Yejin Choi, Ludwig Schmidt
NeurIPS 2024, Datasets and Benchmarks Track Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration
Shangbin Feng, Taylor Sorensen, Yuhan Liu, Jillian Fisher, Chan Young Park, Yejin Choi, Yulia Tsvetkov
EMNLP 2024 CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model Generation
Tong Chen, Akari Asai, Niloofar Mireshghallah, Sewon Min, James Grimmelmann, Yejin Choi, Hannaneh Hajishirzi, Luke Zettlemoyer, Pang Wei Koh
EMNLP 2024 Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Models
Chani Jung, Dongkwan Kim, Jiho Jin, Jiseon Kim, Yeon Seonwoo, Yejin Choi, Alice Oh, Hyunwoo Kim
EMNLP 2024 StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements
Jillian Fisher, Skyler Hallinan, Ximing Lu, Mitchell L Gordon, Zaid Harchaoui, Yejin Choi
EMNLP 2024 How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models
Jaeyoung Lee, Ximing Lu, Jack Hessel, Faeze Brahman, Youngjae Yu, Yonatan Bisk, Yejin Choi, Saadia Gabriel
EMNLP 2024 In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search
Huihan Li, Yuting Ning, Zeyi Liao, Siyuan Wang, Xiang Lorraine Li, Ximing Lu, Wenting Zhao, Faeze Brahman, Yejin Choi, Xiang Ren
EMNLP 2024 Symbolic Working Memory Enhances Language Models for Complex Rule Application
Siyuan Wang, zhongyu wei, Yejin Choi, Xiang Ren
EMNLP 2024 Information-Theoretic Distillation for Reference-less Summarization
Jaehun Jung, Ximing Lu, Liwei Jiang, Faeze Brahman, Peter West, Pang Wei Koh, Yejin Choi
COLM 2024 Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens
Jiacheng Liu, Sewon Min, Luke Zettlemoyer, Yejin Choi, Hannaneh Hajishirzi
COLM 2024 Tuning Language Models by Proxy
Alisa Liu, Xiaochuang Han, Yizhong Wang, Yulia Tsvetkov, Yejin Choi, Noah A. Smith
COLM 2024 Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild
Niloofar Mireshghallah, Maria Antoniak, Yash More, Yejin Choi, Golnoosh Farnadi
COLM 2024 Do Membership Inference Attacks Work on Large Language Models?
Michael Duan, Anshuman Suri, Niloofar Mireshghallah, Sewon Min, Weijia Shi, Luke Zettlemoyer, Yulia Tsvetkov, Yejin Choi, David Evans, Hannaneh Hajishirzi
COLM 2024 CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting
Huihan Li, Liwei Jiang, Nouha Dziri, Xiang Ren, Yejin Choi
COLM 2024 Don't throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decoding
Jiacheng Liu, Andrew Cohen, Ramakanth Pasunuru, Yejin Choi, Hannaneh Hajishirzi, Asli Celikyilmaz
COLM 2024 Position Paper: A Roadmap to Pluralistic Alignment
Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell L Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, Tim Althoff, Yejin Choi
ICML 2024 Structured Chemistry Reasoning with Large Language Models
Siru Ouyang, Zhuosheng Zhang, Bing Yan, Xuan Liu, Yejin Choi, Jiawei Han, Lianhui Qin
ICML 2024 Agent Lumos: Unified and Modular Training for Open-Source Language Agents
Da Yin, Faeze Brahman, Abhilasha Ravichander, Khyathi Chandu, Kai-Wei Chang, Yejin Choi, Bill Yuchen Lin
ACL 2024 Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs
Siyuan Wang, zhongyu wei, Yejin Choi, Xiang Ren
ACL 2024 Impossible Distillation: for Paraphrasing and Summarization: How to Make High-quality Lemonade out of Small, Low-quality Model
Jaehun Jung, Peter West, Liwei Jiang, Faeze Brahman, Ximing Lu, Jillian Fisher, Taylor Sorensen, Yejin Choi
NAACL 2024 MacGyver: Are Large Language Models Creative Problem Solvers?
Yufei Tian, Abhilasha Ravichander, Lianhui Qin, Ronan Le Bras, Raja Marjieh, Nanyun Peng, Yejin Choi, Thomas L. Griffiths, Faeze Brahman
NAACL 2024 JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models
Jillian Fisher, Ximing Lu, Jaehun Jung, Liwei Jiang, Zaid Harchaoui, Yejin Choi
NAACL 2024 UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
Wenting Zhao, Justin T Chiu, Jena D. Hwang, Faeze Brahman, Jack Hessel, Sanjiban Choudhury, Yejin Choi, Xiang Lorraine Li, Alane Suhr
NAACL 2024 NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge
Phillip Howard, Junlin Wang, Vasudev Lal, Gadi Singer, Yejin Choi, Swabha Swayamdipta
NAACL 2024 Findings Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models
Natalie Shapira, Mosh Levy, Seyed Hossein Alavi, Xuhui Zhou, Yejin Choi, Yoav Goldberg, Maarten Sap, Vered Shwartz
EACL 2024 Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties
Taylor Sorensen, Liwei Jiang, Jena Hwang, Sydney Levine, Valentina Pyatkin, Peter West, Nouha Dziri, Ximing Lu, Kavel Rao, Chandra Bhagavatula, Maarten Sap, John Tasioulas, Yejin Choi
AAAI 2024 The Generative AI Paradox: “What It Can Create, It May Not Understand”
*Peter West, *Ximing Lu, *Nouha Dziri, *Faeze Brahman, *Linjie Li, Jena D. Hwang, Liwei Jiang, Jillian Fisher, Abhilasha Ravichander, Khyathi Chandu, Benjamin Newman, Pang Wei Koh, Allyson Ettinger, Yejin Choi
ICLR 2024 The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
Bill Yuchen Lin, Abhilasha Ravichander, Ximing Lu, Nouha Dziri, Melanie Sclar, Khyathi Chandu, Chandra Bhagavatula, Yejin Choi
ICLR 2024 WildChat: 1M ChatGPT Interaction Logs in the Wild
Wenting Zhao, Xiang Ren, Jack Hessel, Claire Cardie, Yejin Choi, Yuntian Deng
ICLR 2024, Spotlight Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement
Linlu Qiu, Liwei Jiang, Ximing Lu, Melanie Sclar, Valentina Pyatkin, Chandra Bhagavatula, Bailin Wang, Yoon Kim, Yejin Choi, Nouha Dziri, Xiang Ren
ICLR 2024, Oral Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory
Niloofar Mireshghallah, Hyunwoo Kim, Xuhui Zhou, Yulia Tsvetkov, Maarten Sap, Reza Shokri, Yejin Choi
ICLR 2024, Spotlight Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
Melanie Sclar, Yejin Choi, Yulia Tsvetkov, Alane Suhr
ICLR 2024 PlaSma: Procedural Knowledge Models for Language-based Planning and Re-Planning
Faeze Brahman, Chandra Bhagavatula, Valentina Pyatkin, Jena D. Hwang, Xiang Lorraine Li, Hirona J. Arai, Soumya Sanyal, Keisuke Sakaguchi, Xiang Ren, Yejin Choi
ICLR 2024 Tailoring Self-Rationalizers with Multi-Reward Distillation
Sahana Ramnath, Brihi Joshi, Skyler Hallinan, Ximing Lu, Liunian Harold Li, Aaron Chan, Jack Hessel, Yejin Choi, Xiang Ren
ICLR 2024 Faith and Fate: Limits of Transformers on Compositionality
*Nouha Dziri, *Ximing Lu, *Melanie Sclar, +Xiang Lorraine Li, +Liwei Jiang, +Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D. Hwang, Soumya Sanyal, Sean Welleck, Xiang Ren, Allyson Ettinger, Zaid Harchaoui, Yejin Choi
NeurIPS 2023, spotlight SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
Bill Yuchen Lin, Yicheng Fu, Karina Yang, Prithviraj Ammanabrolu, Faeze Brahman, Shiyu Huang, Chandra Bhagavatula, Yejin Choi, Xiang Ren
NeurIPS 2023, spotlightLocalized Symbolic Knowledge Distillation for Visual Commonsense Models
Jae Sung Park, Jack Hessel, Khyathi Chandu, Paul Pu Liang, Ximing Lu, Qiuyuan Huang, Peter West, Jianfeng Gao, Ali Farhadi, Yejin Choi
NeurIPS 2023 Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text
Wanrong Zhu, Jack Hessel, Anas Awadalla, Samir Yitzhak Gadre, Jesse Dodge, Alex Fang, Youngjae Yu, Ludwig Schmidt, William Yang Wang, Yejin Choi
NeurIPS 2023, Datasets and Benchmarks Track RealTime QA: What's the Answer Right Now?
Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, Ronan Le Bras, Akari Asai, Xinyan Yu, Dragomir Radev, Noah A. Smith, Yejin Choi, Kentaro Inui
NeurIPS 2023, Datasets and Benchmarks Track
*Project 🍺 Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
Ximing Lu, Faeze Brahman, Peter West, Jaehun Jang, Khyathi Chandu, Abhilasha Ravichander, Lianhui Qin, Prithviraj Ammanabrolu, Liwei Jiang, Sahana Ramnath, Nouha Dziri, Jillian Fisher, Bill Yuchen Lin, Skyler Hallinan, Xiang Ren, Sean Welleck, Yejin Choi
EMNLP 2023 Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements
Jiacheng Liu, Wenya Wang, Dianzhuo Wang, Noah A. Smith, Yejin Choi, Hannaneh Hajishirzi
EMNLP 2023 Crystal: Introspective Reasoners Reinforced with Self-Feedback/a> Jiacheng Liu, Ramakanth Pasunuru, Hannaneh Hajishirzi, Yejin Choi, Asli Celikyilmaz EMNLP 2023 We're Afraid Language Models Aren't Modeling Ambiguity
Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah A. Smith, Yejin Choi
EMNLP 2023 FANToM: A Benchmark for Stress-Testing Theory of Mind in Interactions
Hyunwoo Kim, Melanie Sclar, Xuhui Zhou, Ronan Le Bras, Gunhee Kim, Yejin Choi, Maarten Sap
EMNLP 2023 SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization
Hyunwoo Kim, Jack Hessel, Liwei Jiang, Peter West, Ximing Lu, Youngjae Yu, Pei Zhou, Ronan Le Bras, Malihe Alikhani, Gunhee Kim, Maarten Sap, Yejin Choi
EMNLP 2023 -- Outstanding Paper Award Reading Books is Great, But Not if You Are Driving! Visually Grounded Reasoning about Defeasible Commonsense Norms
Seungju Han, Junhyeok Kim, Jack Hessel, Liwei Jiang, Jiwan Chung, Yejin Son, Yejin Choi, Youngjae Yu
EMNLP 2023 NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation
Peter West, Ronan Le Bras, Taylor Sorensen, Bill Yuchen Lin, Liwei Jiang, Ximing Lu, Khyathi Chandu, Jack Hessel, Ashutosh Baheti, Chandra Bhagavatula, Yejin Choi
EMNLP 2023 Findings What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations
Kavel Rao, Liwei Jiang, Valentina Pyatkin, Yuling Gu, Niket Tandon, Nouha Dziri, Faeze Brahman, Yejin Choi
EMNLP 2023 Findings "You Are An Expert Linguistic Annotator": Limits of LLMs as Analyzers of Abstract Meaning Representation
Allyson Ettinger, Jena D. Hwang, Valentina Pyatkin, Chandra Bhagavatula, Yejin Choi
EMNLP 2023 Findings STEER: Unified Style Transfer with Expert Reinforcement
Skyler Hallinan, Faeze Brahman, Ximing Lu, Jaehun Jung, Sean Welleck, Yejin Choi
EMNLP 2023 FindingsBotPercent: Estimating Bot Populations in Twitter Communities
Zhaoxuan Tan, Shangbin Feng, Melanie Sclar, Herun Wan, Minnan Luo, Yejin Choi, Yulia Tsvetkov
EMNLP 2023 Findings CHAMPAGNE: Learning Real-world Conversation from Large-Scale Web Videos
Seungju Han, Jack Hessel, Nouha Dziri, Yejin Choi, Youngjae Yu
ICCV 2023 Do Embodied Agents Dream of Pixelated Sheep?: Embodied Decision Making using Language Guided World Modelling
Kolby Nottingham, Prithviraj Ammanabrolu, Alane Suhr, Yejin Choi, Hannaneh Hajishirzi, Sameer Singh, Roy Fox
ICML 2023 Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker
Melanie Sclar, Sachin Kumar, Peter West, Alane Suhr, Yejin Choi and Yulia Tsvetkov
ACL 2023 -- Outstanding Paper Award Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step
Liunian Harold Li, Jack Hessel, Youngjae Yu, Xiang Ren, Kai-Wei Chang and Yejin Choi
ACL 2023 Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest
Jack Hessel, Ana Marasović, Jena D. Hwang, Lillian Lee, Jeff Da, Rowan Zellers, Robert Mankoff, Yejin Choi
ACL 2023 -- Best Paper Award ClarifyDelphi: Reinforced Clarification Questions with Defeasibility Rewards for Social and Moral Situations
Valentina Pyatkin, Jena D. Hwang, Vivek Srikumar, Ximing Lu, Liwei Jiang, Yejin Choi, Chandra Bhagavatula
ACL 2023 I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation
Chandra Bhagavatula, Jena D. Hwang, Doug Downey, Ronan Le Bras, Ximing Lu, Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, Yejin Choi
ACL 2023 SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created through Human-Machine Collaboration
Hwaran Lee, Seokhee Hong, Joonsuk Park, Takyoung Kim, Meeyoung Cha, Yejin Choi, BYOUNGPIL KIM, Gunhee Kim, Eun-Ju Lee, Yong Lim, Alice Oh, Sangchul Park and Jung-Woo Ha
ACL 2023 An AI Dungeon Master's Guide: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons
Pei Zhou, Andrew Zhu, Jennifer Hu, Jay Pujara, Xiang Ren, Chris Callison-Burch, Yejin Choi and Prithviraj Ammanabrolu
ACL 2023 Detoxifying Text with MaRCo: Controllable Revision with Experts and Anti-Experts
Skyler Hallinan, Alisa Liu, Yejin Choi and Maarten Sap
ACL 2023 Are Machine Rationales (Not) Useful to Humans? Measuring and Improving Human Utility of Free-text Rationales
Brihi Joshi, Ziyi Liu, Sahana Ramnath, Aaron Chan, Zhewei Tong, Shaoliang Nie, Qifan Wang, Yejin Choi and Xiang Ren
ACL 2023 From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Models
Julia Mendelsohn, Ronan Le Bras, Yejin Choi and Maarten Sap
ACL 2023 Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation
Kung-Hsiang Huang, Kathleen McKeown, Preslav Nakov, Yejin Choi and Heng Ji
ACL 2023 REV: Information-Theoretic Evaluation of Free-Text Rationales
Hanjie Chen, Faeze Brahman, Xiang Ren, Yangfeng Ji, Yejin Choi and Swabha Swayamdipta
ACL 2023 Commonsense Knowledge Transfer for Pre-trained Language Models
Wangchunshu Zhou, Ronan Le Bras and Yejin Choi
ACL Findings 2023 Modularized Encoder-Decoder Models for Flexible Sequence-to-Sequence Compression
Wangchunshu Zhou, Ronan Le Bras and Yejin Choi
ACL Findings 2023 Multimodal Knowledge Alignment with Reinforcement Learning
Youngjae Yu, Jiwan Chung, Heeseung Yun, Jack Hessel, Jae Sung Park, Ximing Lu, Rowan Zellers, Prithviraj Ammanabrolu, Ronan Le Bras, Gunhee Kim, Yejin Choi
CVPR 2023 Influence Diagnostics under Self-concordance
Jillian Fisher, Lang Liu, Krishna Pillutla, Yejin Choi, and Zaid Harchaoui
AISTAT 2023 Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions
Emily Allaway, Jena D. Hwang, Chandra Bhagavatula, Kathleen McKeown, Doug Downey, Yejin Choi
EACL 2023 Generating Sequences by Learning to [Self-]Correct
Sean Welleck*, Ximing Lu*, Peter West+, Faeze Brahman+, Tianxiao Shen, Daniel Khashabi, Yejin Choi
ICLR 2023 Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization
Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kianté Brantley, Jack Hessel, Rafet Sifa, Christian Bauckhage, Hannaneh Hajishirzi, Yejin Choi
ICLR 2023
*Project 🍏 The Curious Case of Commonsense Intelligence
Yejin Choi
Daedalus's special issue on AI & Society, 2022 Quark: Controllable Text Generation with Reinforced Unlearning
Ximing Lu, Sean Welleck, Liwei Jiang, Jack Hessel, Lianhui Qin, Peter West, Prithviraj Ammanabrolu, Yejin Choi
NeurIPS 2022 COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics
Lianhui Qin, Sean Welleck, Daniel Khashabi, Yejin Choi
NeurIPS 2022 NaturalProver: Grounded Mathematical Proof Generation with Language Models
Sean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, Yejin Choi
NeurIPS 2022 Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation
Melanie Sclar, Peter West, Sachin Kumar, Yulia Tsvetkov and Yejin Choi
EMNLP 2022 Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations
Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras and Yejin Choi
EMNLP 2022 Rainier: Reinforced Knowledge Introspector for Commonsense Question Answering
Jiacheng Liu, Skyler Hallinan, Ximing Lu, Pengfei He, Sean Welleck, Hannaneh Hajishirzi, Yejin Choi
EMNLP 2022 Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs
Maarten Sap, Ronan Le Bras, Daniel Fried and Yejin Choi
EMNLP 2022 ProsocialDialog: A Prosocial Backbone for Conversational Agents
Hyunwoo Kim, Youngjae Yu, Liwei Jiang, Ximing Lu, Daniel Khashabi, Gunhee Kim, Yejin Choi and Maarten Sap
EMNLP 2022
*Data Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ Tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Maitreya Patel, Kuntal Kumar Pal, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Shailaja Keyur Sampat, Savan Doshi, Siddhartha Mishra, Sujan Reddy, Sumanta Patro, Tanay Dixit, Xudong Shen, Chitta Baral, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi and Daniel Khashabi
EMNLP 2022
*Project Twist Decoding: Diverse Generators Guide Each Other
Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Hao Peng, Ximing Lu, Dragomir Radev, Yejin Choi, Noah A. Smith
EMNLP 2022 Toward Reproducible and Standardized Human Evaluation for Text Generation
Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith and Daniel Weld
EMNLP 2022
*Project WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation
Alisa Liu, Swabha Swayamdipta, Noah A. Smith, Yejin Choi
EMNLP 2022 Findings NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation
Phillip Howard, Gadi Singer, Vasudev Lal, Yejin Choi and Swabha Swayamdipta
EMNLP 2022 Findings NaturalAdversaries: Can Natural Adversaries Be As Effective As Artificial Adversaries?
Saadia Gabriel, Hamid Palangi and Yejin Choi
EMNLP 2022 Findings The Abduction of Sherlock Holmes: A Dataset for Visual Abductive Reasoning
Jack Hessel, Jena D. Hwang, Jae Sung Park, Rowan Zellers, Chandra Bhagavatula, Anna Rohrbach, Kate Saenko, Yejin Choi
ECCV 2022 Information-Theoretic Measures of Dataset Difficulty
Kawin Ethayarajh, Yejin Choi, Swabha Swayamdipta
ICML 2022 -- Outstanding Paper Award MERLOT Reserve: Multimodal Neural Script Knowledge through Vision and Language and Sound
Rowan Zellers, Jiasen Lu, Ximing Lu, Youngjae Yu, Yanpeng Zhao, Mohammadreza Salehi, Aditya Kusupati, Jack Hessel, Ali Farhadi, Yejin Choi
CVPR 2022
*Project Quantifying the Narrative Flow of Imagined versus Autobiographical Stories
Maarten Sap, Anna Jafarpour, Yejin Choi, Noah A. Smith, James W. Pennebaker, Eric Horvitz
PNAS 2022 It’s not Rocket Science: Interpreting Figurative Language in Narratives
Tuhin Chakrabarty , Yejin Choi and Vered Shwartz
TACL 2022 Aligning to Social Norms and Values in Interactive Narratives
Prithviraj Ammanabrolu, Liwei Jiang, Maarten Sap, Hanna Hajishirzi, Yejin Choi
NAACL 2022 Symbolic Knowledge Distillation: from General Language Models to Commonsense Models
Peter West, Chandra Bhagavatula, Jack Hessel, Jena D. Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, Yejin Choi
NAACL 2022 NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
Ximing Lu, *Sean Welleck, *Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah Smith, Yejin Choi
NAACL 2022 -- Best Paper Award Promp Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts
Daniel Khashabi, Shane Lyu, Sewon Min, Lianhui Qin, Kyle Richardson, Sameer Singh, Sean Welleck, Hannaneh Hajishirzi, Tushar Khot, Ashish Sabharwal and Yejin Choi
NAACL 2022 Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand
Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Lavinia Dunagan, Jacob Morrison, Alexander R. Fabbri, Yejin Choi, Noah A. Smith
NAACL 2022 Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection
Maarten Sap, Swabha Swayamdipta, Laura Vianna, Xuhui Zhou, Yejin Choi, Noah A. Smith
NAACL 2022 Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer
Yanpeng Zhao, Jack Hessel, Youngjae Yu, Ximing Lu, Rowan Zellers, Yejin Choi
NAACL 2022 Transparent Human Evaluation for Image Captioning
Jungo Kasai, Keisuke Sakaguchi, Lavinia Dunagan, Jacob Morrison, Ronan Le Bras, Yejin Choi, Noah A. Smith
NAACL 2022 Reframing Human-AI Collaboration for Generating Free-Text Explanations
Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark Riedl, Yejin Choi
NAACL 2022 Exposing the Limits of Video-Text Models through Contrast Sets
Jae Sung Park, Sheng Shen, Ali Farhadi, Trevor Darrell, Yejin Choi, Anna Rohrbach
NAACL 2022 🎃 Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text
Yao Dou, Maxwell Forbes, Rik Koncel-Kedziorski, Noah A. Smith, Yejin Choi
ACL 2022 Misinfo Reaction Frames: Reasoning about Readers' Reactions to News Headlines
Saadia Gabriel, Skyler Hallinan, Maarten Sap, Pemi Nguyen, Franziska Roesner, Eunsol Choi, Yejin Choi
ACL 2022 Generated Knowledge Prompting for Commonsense Reasoning
Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi
ACL 2022 Reframing Instructional Prompts to GPTk's Language
Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, Hannaneh Hajishirzi
ACL 2022, Findings Probing Factually Grounded Content Transfer with Factual Ablation
Peter West, Chris Quirk, Michel Galley, Yejin Choi
ACL 2022, Findings Symbolic Brittleness in Sequence Models: on Systematic Generalization in Symbolic Mathematics
Sean Welleck, Peter West, Jize Cao, Yejin Choi
AAAI 2022 🍷MERLOT: Multimodal Neural Script Knowledge Models
Rowan Zellers*, Ximing Lu*, Jack Hessel*, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, Yejin Choi
NeurIPS 2021, Oral (1%) MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Yejin Choi, Zaid Harchaoui
NeurIPS 2021, Oral (1%) -- Outstanding Paper Award Divergence Frontiers for Generative Models: Sample Complexity, Quantization Level, and Frontier Integral
Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaid Harchaoui
NeurIPS 2021 NaturalProofs: Mathematical Theorem Proving in Natural Language
Sean Welleck, Jiacheng Liu, Ronan Le Bras, Hannaneh Hajishirzi, Yejin Choi, Kyunghyun Cho
NeurIPS 2021 Datasets and Benchmarks Track, Oral (1%) CommonsenseQA 2.0: Exposing the Limits of AI through Gamification
Alon Talmor, Ori Yoran, Ronan Le Bras, Chandra Bhagavatula, Yoav Goldberg, Yejin Choi, Jonathan Berant
NeurIPS 2021 Datasets and Benchmarks Track CLIPScore: A Reference-free Evaluation Metric for Image Captioning
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras and Yejin Choi
EMNLP 2021 Moral Stories: Learning to Reason about Norms, Intents, Actions, and their Consequences from Short Narratives
Denis Emelin, Ronan Le Bras, Jena D. Hwang, Maxwell Forbes and Yejin Choi
EMNLP 2021 Conversational Multi-hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules
Forough Arabshahi, Jennifer Lee, Antoine Bosselut, Yejin Choi and Tom Mitchell
EMNLP 2021 Contrastive Explanations for Model Interpretability
Alon Jacovi, Swabha Swayamdipta, Shauli Ravfogel, Yanai Elazar, Yejin Choi and Yoav Goldberg
EMNLP 2021 Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right
Ari Holtzman*, Peter West*, Vered Shwartz, Yejin Choi and Luke Zettlemoyer
EMNLP 2021 proScript: Partially Ordered Scripts Generation
Keisuke Sakaguchi, Chandra Bhagavatula, Ronan Le Bras, Niket Tandon, Peter Clark and Yejin Choi
EMNLP Findings 2021 Analyzing Commonsense Emergence in Few-shot Knowledge Models
Jeff Da, Ronan Le Bras, Ximing Lu, Yejin Choi, Antoine Bosselut
AKBC 2021 DExperts: On-the-Fly Controlled Text Generation with Experts and Anti-Experts
Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah Smith and Yejin Choi
ACL 2021 PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World
Rowan Zellers, Ari Holtzman, Matthew Peters, Roozbeh Mottaghi, Aniruddha Kembhavi, Ali Farhadi and Yejin Choi
ACL 2021 TIMEDIAL: Temporal Commonsense Reasoning in Dialog
Lianhui Qin, Aditya Gupta, Shyam Upadhyay, Luheng He, Yejin Choi and Manaal Faruqui
ACL 2021 Reflective Decoding: Beyond Unidirectional Generation with Off-the-shelf Language Models
Peter West, Ximing Lu, Ari Holtzman, Chandra Bhagavatula, Jena D. Hwang and Yejin Choi
ACL 2021 Edited Media Understanding Frames: Reasoning About the Intent and Implications of Visual Misinformation
Jeff Da, Maxwell Forbes, Rowan Zellers, Anthony Zheng, Jena D. Hwang, Antoine Bosselut and Yejin Choi
ACL 2021 On-the-Fly Attention Modulation for Neural Generation
Yue Dong, Chandra Bhagavatula, Ximing Lu, Jena D. Hwang, Antoine Bosselut, Jackie Chi Kit Cheung and Yejin Choi
ACL 2021 Findings Go Figure: A Meta Evaluation of Factuality in Summarization
Saadia Gabriel, Asli Celikyilmaz, Rahul Jha, Yejin Choi and Jianfeng Gao
ACL 2021 Findings VinVL: Making Visual Representations Matter in Vision-Language Models
Pengchuan Zhang*, Xiujun Li*, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, Jianfeng Gao
CVPR 2021 TuringAdvice: A Generative and Dynamic Evaluation of Language Use
Rowan Zellers, Ari Holtzman, Elizabeth Clark, Lianhui Qin, Ali Farhadi, Yejin Choi
NAACL 2021
*Project Page NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints
Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi
NAACL 2021 "I'm Not Mad": Commonsense Implications of Negation and Contradiction
Liwei Jiang, Antoine Bosselut, Chandra Bhagavatula, Yejin Choi
NAACL 2021 Discourse Understanding and Factual Consistency in Abstractive Summarization
Saadia Gabriel, Antoine Bosselut, Jeff Da, Ari Holtzman, Jan Buys, Kyle Lo, Asli Celikyilmaz, Yejin Choi
EACL 2021 Challenges in Automated Debiasing for Toxic Language Detection
Xuhui Zhou, Maarten Sap, Swabha Swayamdipta, Noah A. Smith, and Yejin Choi
EACL 2021 (Comet-) Atomic 2020: On Symbolic and Neural Commonsense Knowledge Graphs
Jena Hwang*, Chandra Bhagavatula*, Ronan Le Bras, Jeff Da, Keisuke Sakaguchi, Antoine Bosselut, Yejin Choi
AAAI 2021 🦄 Unicorn on Rainbow: A Universal Commonsense Reasoning Model on a New Multitask Benchmark
Nicholas Lourie, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi
AAAI 2021 Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering
Antoine Bosselut, Ronan Le Bras, Yejin Choi
AAAI 2021 Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision
Faeze Brahman, Vered Shwartz, Rachel Rudinger, Yejin Choi
AAAI 2021 Paragraph-level Commonsense Transformers with Recurrent Memory
Saadia Gabriel, Chandra Bhagavatula, Vered Shwartz, Ronan Le Bras, Maxwell Forbes, Yejin Choi
AAAI 2021 MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations
Yao Dou, Maxwell Forbes, Ari Holtzman, Yejin Choi
AAAI 2021 Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes
Nicholas Lourie, Ronan Le Bras, Yejin Choi
AAAI 2021 🔮 Social Chemistry 101: Learning to Reason about Social and Moral Norms
Maxwell Forbes, Jena D. Hwang, Vered Shwartz, Maarten Sap and Yejin Choi
EMNLP, 2020 Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning (DeLorean)
Lianhui Qin, Vered Shwartz, Peter West, Chandra Bhagavatula, Jena D. Hwang, Ronan Le Bras, Antoine Bosselut and Yejin Choi
EMNLP, 2020 PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction
Xinyao Ma, Maarten Sap, Hannah Rashkin and Yejin Choi
EMNLP, 2020 Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith and Yejin Choi
EMNLP, 2020 PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking
Hannah Rashkin, Asli Celikyilmaz, Yejin Choi and Jianfeng Gao
EMNLP, 2020 Unsupervised Commonsense Question Answering with Self-Talk
Vered Shwartz, Peter West, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi
EMNLP, 2020 Thinking Like a Skeptic: Defeasible Inference in Natural Language
Rachel Rudinger, Vered Shwartz, Jena D. Hwang, Chandra Bhagavatula, Maxwell Forbes, Ronan Le Bras, Noah A. Smith and Yejin Choi
Findings of EMNLP, 2020 RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi and Noah A. Smith
Findings of EMNLP, 2020
*Demo Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs
Ana Marasovic, Chandra Bhagavatula, Jae sung Park, Ronan Le Bras, Noah A. Smith and Yejin Choi
Findings of EMNLP, 2020 CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
Yuchen Lin, Wangchunshu Zhou, Ming Shen, Pei Zhou, Chandra Bhagavatula, Yejin Choi, Xiang Ren
Findings of EMNLP, 2020
*Project Page Generative Data Augmentation for Commonsense Reasoning
Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi and Doug Downey
Findings of EMNLP, 2020 Visual Comet: Reasoning about the Dynamic Context of a Still Image
Jae Sung Park, Chandra Bhagavatula, Roozbeh Mottaghi, Ali Farhadi, Yejin Choi
ECCV, 2020
*Project Page Oscar: Object-Semantics Aligned Pre-training for Vision-and-Language Tasks
Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, Yejin Choi, Jianfeng Gao
ECCV, 2020 Adversarial Filters of Dataset Biases
Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew E. Peters, Ashish Sabharwal, Yejin Choi
ICML, 2020 Social Bias Frames: Reasoning about Social and Power Implications of Language
Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A Smith & Yejin Choi
ACL, 2020
*Project Page Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models
Maarten Sap, Eric Horvitz, Yejin Choi, Noah A Smith & James W Pennebaker
ACL, 2020
*Project Page Procedural Reading Comprehension with Attribute-Aware Context Flow
Aida Amini, Antoine Bosselut, Bhavana Dalvi Mishra, Yejin Choi, Hannaneh Hajishirzi
AKBC, 2020 -- best paper nomination The Curious Case of Neural Text **De**generation
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, Yejin Choi
ICLR, 2020 Abductive Commonsense Reasoning
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Wen-tau Yih and Yejin Choi
ICLR, 2020 WinoGrande: An Adversarial Winograd Schema Challenge at Scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi
AAAI, 2020 -- Outstanding Paper Award (given to the single best paper)
*Leaderboard, Annoucement PIQA: Reasoning about Physical Commonsense in Natural Language
Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, Yejin Choi
AAAI, 2020
*Leaderboard Commonsense Knowledge Base Completion with Structural and Semantic Context
Chaitanya Malaviya, Chandra Bhagavatula, Antoine Bosselut, Yejin Choi
AAAI, 2020Do Neural Language Models Overcome Reporting Bias?
Vered Shwartz and Yejin Choi
COLING, 2020 Defending Against Neural Fake News (Grover)
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, Yejin Choi
NeurIPS, 2019
*Blog! (with additional experiments & analysis)
*Grover Demo!
*Project Page BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle
Peter West, Ari Holtzman, Jan Buys and Yejin Choi
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019
*Project Page Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning
Lifu Huang, Ronan Le Bras, Chandra Bhagavatula and Yejin Choi
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019
*Project Page & Leaderboard Counterfactual Story Reasoning and Generation
Lianhui Qin, Antoine Bosselut, Ari Holtzman, Chandra Bhagavatula, Elizabeth Clark and Yejin Choi
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019
*Code & Data Social IQa: Commonsense Reasoning about Social Interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras and Yejin Choi
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019
*Project Page & Leaderboard Robust Navigation with Language Pretraining and Stochastic Sampling
Xiujun Li, Chunyuan Li, Qiaolin Xia, Yonatan Bisk, Asli Celikyilmaz, Jianfeng Gao, Noah A. Smith and Yejin Choi
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019 Early Fusion for Goal Directed Robotic Vision
Aaron Walsman, Yonatan Bisk, Saadia Gabriel, Dipendra Misra, Yoav Artzi, Yejin Choi, Dieter Fox
International Conference on Intelligent Robots and Systems (IROS), 2019-- best paper nomination Do Neural Language Representations Learn Physical Commonsense?
Maxwell Forbes, Ari Holtzman, Yejin Choi
Conference of the Cognitive Science Society (CogSci), 2019
*Project Page COMET: Commonsense Transformers for Knowledge Graph Construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz and Yejin Choi
Association for Computational Linguistics (ACL), 2019
*Demo Hellaswag: Can a Machine Really Finish Your Sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi and Yejin Choi
Association for Computational Linguistics (ACL), 2019
*Project Page & Leaderboard Featured in ZdNet: "No, This AI can't finish your sentence" The Risk of Racial Bias in Hate Speech Detection
Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi and Noah A. Smith
Association for Computational Linguistics (ACL), 2019 -- best paper nomination Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
Lianhui Qin, Michel Galley, Chris Brockett, Xiaodong Liu, Xiang Gao, Bill Dolan, Yejin Choi and Jianfeng Gao
Association for Computational Linguistics (ACL), 2019
*Code & Data From Recognition to Cognition: Visual Commonsense Reasoning (VCR)
Rowan Zellers, Yonatan Bisk, Ali Farhadi, Yejin Choi.
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
*Project Page & Leaderboard Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation
Liyiming Ke, Xiujun Li, Yonatan Bisk, Ari Holtzman, Zhe Gan, Jingjing Liu, Jianfeng Gao, Yejin Choi and Siddhartha Srinivasa
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
*Video Demo ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
Maarten Sap, Ronan LeBras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A Smith and Yejin Choi
Association for the Advancement of Artificial Intelligence (AAAI), 2019.
*Demo at AI2, *Project Page at UW MathQA Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi and Hannaneh Hajishirzi.
North American Chapter of the Association for Computational Linguistics (NAACL), 2019
*Project Page & Leaderboard Benchmarking Hierarchical Script Knowledge
Yonatan Bisk, Jan Buys, Karl Pichotta and Yejin Choi.
North American Chapter of the Association for Computational Linguistics (NAACL), 2019 DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension
Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, Claire Cardie
Transactions of the Association for Computational Linguistics (TACL), 2019.
*Leaderboard SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz and Yejin Choi
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.
*Project Page *Leaderboard QuAC: Question Answering in Context
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang and Luke Zettlemoyer
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.
*Leaderboard Neural Metaphor Detection in Context
Ge Gao, Eunsol Choi, Yejin Choi and Luke Zettlemoyer
Conference on Empirical Methods in Natural Language Processing (EMNLP), short paper, 2018. Modeling Naive Psychology of Characters in Simple Commonsense Stories
Hannah Rashkin, Antoine Bosselut, Maarten Sap, Kevin Knight and Yejin Choi
Association for Computational Linguistics (ACL), 2018.
*Project Page (with an online dataset browser!) Learning to Write with Cooperative Discriminators
Ari Holtzman, Jan Buys, Maxwell Forbes, Antoine Bosselut, David Golub and Yejin Choi
Association for Computational Linguistics (ACL), 2018. Event2Mind: Commonsense Inference on Events, Intents, and Reactions
Hannah Rashkin, Maarten Sap, Emily Allaway, Noah A. Smith and Yejin Choi
Association for Computational Linguistics (ACL), 2018.
*Project Page (with an online dataset browser!) Ultra-Fine Entity Typing
Eunsol Choi, Omer Levy, Yejin Choi and Luke Zettlemoyer
Association for Computational Linguistics (ACL), 2018.
*Project Page Sounding Board: A User-Centric and Content-Driven Social Chatbot
Hao Fang, Hao Cheng, Maarten Sap, Elizabeth Clark, Ari Holtzman, Yejin Choi, Noah A. Smith, and Mari Ostendorf
North American Chapter of the Association for Computational Linguistics (NAACL), demo paper, 2018. Discourse-Aware Neural Rewards for Coherent Text Generation
Antoine Bosselut, Asli Celikyilmaz, Xiaodong He, Jianfeng Gao, Po-sen Huang, and Yejin Choi
North American Chapter of the Association for Computational Linguistics (NAACL), 2018. Deep Communicating Agents for Abstractive Summarization
Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, and Yejin Choi
North American Chapter of the Association for Computational Linguistics (NAACL), 2018. Neural Poetry Translation
Marjan Ghazvininejad, Yejin Choi, and Kevin Knight
North American Chapter of the Association for Computational Linguistics (NAACL), short paper, 2018. Neural Motifs: Scene Graph Parsing with Global Context
Rowan Zellers, Mark Yatskar, Sam Thomson, and Yejin Choi
Conference on Computer Vision and Pattern Recognition (CVPR), 2018 Simulating Action Dynamics with Neural Process Networks
Antoine Bosselut, Omer Levy, Ari Holtzman, Corin Ennis, Dieter Fox, and Yejin Choi
International Conference on Learning Representations (ICLR), 2018.
[Podcast] Learning Interpretable Spatial Operations in a Rich 3D Blocks World
Yonatan Bisk, Kevin J. Shih, Yejin Choi, and Daniel Marcu
Association for the Advancement of Artificial Intelligence (AAAI), 2018. Bridging HMMs and RNNs through Architectural Transformations
Jan Buys, Yonatan Bisk, Yejin Choi
IRASL workshop @ NeurIPS, 2018. Zero-Shot Activity Recognition with Verb Attribute Induction
Rowan Zellers and Yejin Choi
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017.
*Project Page Dynamic Entity Representations in Neural Language Models
Yangfeng Ji, Chenhao Tan, Sebastian Martschat, Yejin Choi and Noah A. Smith
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017. Truth of Varying Shades: On Political Fact-Checking and Fake News
Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova and Yejin Choi
Conference on Empirical Methods in Natural Language Processing (EMNLP), short paper, 2017.
*Project Page Connotation Frames of Agency and Power in Modern Films
Maarten Sap, Marcella Cindy Prasettio, Ariel Holtzman, Hannah Rashkin and Yejin Choi
Conference on Empirical Methods in Natural Language Processing (EMNLP), short paper, 2017.
*Project Page *Demo Page Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi and Luke Zettlemoyer
Association for Computational Linguistics (ACL), 2017.
*Project Page Verb Physics: Relative Physical Knowledge of Actions and Objects
Maxwell Forbes and Yejin Choi
Association for Computational Linguistics (ACL), 2017.
*Project Page Multilingual Connotation Frames: A Case Study on Social Media for Targeted Sentiment Analysis and Forecast
Hannah Rashkin, Eric Bell, Yejin Choi, and Svitlana Volkova
Association for Computational Linguistics (ACL), short paper, 2017.
*Project Page The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze Task
Roy Schwartz, Maarten Sap, Yannis Konstas, Li Zilles, Yejin Choi and Noah A. Smith
Conference on Computational Natural Language Learning (CoNLL), 2017. Story Cloze Task: UW NLP System
Roy Schwartz, Maarten Sap, Yannis Konstas, Li Zilles, Yejin Choi and Noah A. Smith
LSDSem 2017 shared task (Best performing system), 2017. Globally Coherent Text Generation with Neural Checklist Models
Chloé Kiddon, Luke Zettlemoyer, and Yejin Choi
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016. Generating Topical Poetry
Marjan Ghazvininejad, Xing Shi, Yejin Choi, and Kevin Knight
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016. Connotation Frames: A Data-Driven Investigation
Hannah Rashkin, Sameer Singh, and Yejin Choi
Association for Computational Linguistics (ACL), 2016.
*Project Page Learning Prototypical Event Structure from Photo Albums
Antoine Bosselut, Jianfu Chen, David Warren, Hannaneh Hajishirzi, and Yejin Choi
Association for Computational Linguistics (ACL), 2016.
*Project Page Document-level Sentiment Inference with Social, Faction, and Discourse Context
Eunsol Choi, Hannah Rashkin, Luke Zettlemoyer, and Yejin Choi
Association for Computational Linguistics (ACL), 2016.
*Project Page Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects
Hessam Bagherinezhad, Hannaneh Hajishirzi, Yejin Choi, and Ali Farhadi
Association for the Advancement of Artificial Intelligence (AAAI), 2016.
Selected Publications (2011 - 2015):
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing
Hamid Izadinia, Fereshteh Sadeghi, Santosh Kumar Divvala, Hannaneh Hajishirzi, Yejin Choi, and Ali Farhadi
International Conference on Computer Vision (ICCV), 2015. (oral)
Mise en Place: Unsupervised Interpretation of Instructional Recipes
Chloé Kiddon, Ganesa Thandavam Ponnuraj, Luke Zettlemoyer, and Yejin Choi
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015.
TreeTalk: Composition and Compression of Trees for Image Descriptions
Polina Kuznetsova, Vicente Ordonez, Tamara Berg, and Yejin Choi.
Transaction of Association for Computational Linguistics (TACL), 2014. (presented at EMNLP 2014) From Large Scale Image Categorization to Entry-Level Categories.
Vicente Ordonez, Jia Deng, Yejin Choi, Alexander C Berg, and Tamara L Berg
International Conference on Computer Vision (ICCV), 2013. -- Marr Prize (best paper award) Connotation Lexicon: A Dash of Sentiment Beneath the Surface Meaning.
Song Feng, Jun Seok Kang, Polina Kuznetsova, and Yejin Choi.
Association for Computational Linguistics (ACL), 2013.
*Project Page *Data: Connotation lexiconFeatured in Fast Company BabyTalk: Understanding and generating simple image descriptions
Girish Kulkarni, Visruth Premraj, Vicente Ordonez, Sagnik Dhar, Siming Li, Yejin Choi, Alexander C Berg, Tamara L Berg
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. -- Longuet Higgins Prize (test of time award) at CVPR 2021 Finding Deceptive Opinion Spam by Any Stretch of the Imagination.
Myle Ott, Yejin Choi, Claire Cardie, and Jeffrey Hancock.
Association for Computational Linguistics (ACL), 2011. -- Test of Time Award at ACL 2021Featured in WNBC News for New York (Sep 2012, 5pm news); NHPR Radio (Sep 22, 2011); Bloomberg Business Week (Oct 2011); NY Times (Aug 19, 2011);
Full List of Publications:
Google Scholar
Semantic Scholar
Recent Teaching:
CSE 517 + 447 (Grad + Undergrad) Natural Language Processing --- an LLM edition [Winter 2024]
CSE 599 D1 (Grad) Exploration on Language, Knowledge, and Reasoning [Winter 2023]
CSEP 517 (Professional MS) Natural Language Processing [Winter 2021]
CSE 447 (Undergrad) Natural Language Processing [Winter 2020]
CSE 517 (Grad) Natural Language Processing [Winter 2019]
CSE 481 N (Undergrad Capstone) Natural Language Processing [Spring 2018]
CSE 447 (Undergrad) Natural Language Processing [Winter 2018]
CSE 481 N (Undergrad Capstone) Natural Language Processing [Spring 2017]
CSE 517 (Grad) Natural Language Processing [Winter 2017]
CSE 490 U (Undergrad) Natural Language Processing [Spring 2016]
CSEP 517 (Professional MS) Natural Language Processing [Autumn 2015]
CSE 599 (Grad) Advanced NLP [Spring 2015]
CSE 517 (Grad) Natural Language Processing [Winter 2015]
xlab:
- Current members:
Post Doc:
Abhilasha Ravichander (Young Investigator @ AI2 / UW)
Valentina Pyatkin (Young Investigator @ AI2)
Pan Lu - co-advised with James Zou
Yining Hong
PhD:
Jae Sung (James) Park - co-advised with Ali Farhadi and Ranjay Krishna
Liwei Jiang
Alisa Liu - co-advised with Noah Smith
Jiacheng (Gary) Liu - co-advised with Hannaneh Hajishirzi
Jillian Fisher - co-advised with Thomas Richardson
Melanie Sclar - co-advised with Yulia Tsvetkov
Jaehun Jung
Taylor Sorensen
Ximing (Gloria) Lu
Ben Newman
Anas Awadalla - co-advised with Ludwig Schmidt
Linjie (Lidsey) Li - co-advised with Ranjay Krishna
Seungju Han
Etash Guha- co-advised with Ludwig Schmidt
Undergrad: - Former members:
Research Scientist:
Sydney Levine (-> Assistant Professor @ NYU after a gap year at Google DeepMind)
Post Doc:
Hyunwoo Kim (->Assistant Professor at KAIST after a gap year at NVIDIA)
Fatemeh (Niloofar) Mireshghallah (-> Assistant Professor at CMU LTI/EPP after a gap year at Meta FAIR)
Chan Young Park (-> Assistant Professor at UT Austin ISchool after a gap year at MSR)
Faeze Brahman (Research Scientist @ AI2)
Tianxiao Shen (-> Research Scientist at Google DeepMind)
Yuntian Deng (-> Assistant Professor at Waterloo)
Mitchell L Gordon (-> Assitant Professor at MIT)
Sean Welleck (-> Assistant Professor at CMU)
Nouha Dziri (-> Research Scientist @ AI2)
Yuchen (Bill) Lin (-> Research Scientist @ AI2)
Prithviraj Ammanabrolu (-> Assistant Professor at UCSD after a gap year at MosaicML)
Alane Suhr (-> Assistant Professor @ UC Berkeley)
Xiang Lorraine Li (-> Assistant Professor @ University of Pittsburgh)
Youngjae Yu (-> Assistant Professor @ Yonsei University)
Daniel Khashabi (-> Assistant Professor @ JHU)
Swabha Swayamdipta (-> Assistant Professor @ USC)
Vered Shwartz (-> Assistant Professor @ UBC)
Jack Hessel (-> Research Scientist @ AI2 -> Samaya AI)
Rachel Rudinger (-> Assistant Professor @ University of Maryland)
Yonatan Bisk (-> Assistant Professor @ CMU LTI/Robotics)
Jan Buys (-> Assistant Professor @ University of Cape Town)
Yannis Konstas (-> Assistant Professor @ Heriot-Watt University)
PhD:
Peter West (-> Assistant Professor at UBC after a gap year at Stanford)
Xiujun Li (-> Research Scientist @ Apple)
Lianhui (Karen) Qin (-> Assistant Professor at UCSD)
Saadia Gabriel (->Assistant Professor @ UCLA after a gap year at MIT and NYU)
Maarten Sap (-> Assistant Professor at CMU)
Rowan Zellers (-> OpenAI)
Maxwell Forbes (-> Wandering around the world)
Antoine Bosselut (-> Assistant Professor @ EPFL)
Hannah Rashkin (-> Research Scientist @ Google Research)
Eunsol Choi (-> Assistant Professor @ Texas Austin)
Chloé Kiddon (-> Google)
Jun Seok Kang (-> Blink Health)
Song Feng (-> Research Staff Member @ IBM Research)
Polina Kuznetsova (-> Research Scientist @ Facebook)
Ritwik Banerjee (-> Research Assistant Professor @ Stony Brook University)
Undergrad:
Kavel Rao
Jenny Liang (-> PYI @ AI2 -> PhD @ CMU)
Jeff Da (-> PYI @ AI2)
Yao Dou (-> PhD @ GaTech)
Xinyao (Michelle) Ma (-> Pinterest)
Ge Gao (-> PhD @ Cornell)
Emily Louise Allaway (-> PhD @ Columbia)
Marcella Cindy Prasetio (-> MS @ Stanford)
Ryan Benmalek (-> PhD @ Cornell)
Pooja Sethi (-> Facebook)
Short Bio:
Yejin Choi is the Dieter Schwarz Foundation Professor and Senior Fellow at the Department of Computer Science at Stanford University and the Stanford Institute for Human-Centered Artificial Intelligence (HAI) respectively. She is currently Senior Director at NVIDIA, and previously Professor at UW and Senior Director at AI2. Choi is MacArthur Fellow (class of 2022), AI2050 Senior Fellow (class of 2024), and named among Time100 Most Influential People in AI in 2023. In addition, Choi is a co-recipient of 2 Test-of-Time awards and 8 Best and Outstanding Paper Awards at top AI conferences including ACL, ICML, NeurIPS, ICCV, CVPR, and AAAI, the Borg Early Career Award (BECA) in 2018, the inaugural Alexa Prize Challenge in 2017, and IEEE AI’s 10 to Watch in 2016. Choi was a main stage speaker at TED 2023, and a keynote speaker for a dozen conferences across several AI disciplines including ACL, CVPR, ICLR, MLSys, VLDB, WebConf, and AAAI. Her current research interests include fundamental limits and capabilities of large language models, alternative training recipes for language models, symbolic methods for neural networks, reasoning and knowledge discovery, moral norms and values, pluralistic alignment, and AI safety.
Personal:
Scuba!