# Yejin Choi

> Source: https://aiwiki.ai/wiki/yejin_choi
> Updated: 2026-07-07
> Categories: AI Research, People
> License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
> From AI Wiki (https://aiwiki.ai), the free encyclopedia of artificial intelligence. Reuse freely with attribution to "AI Wiki (aiwiki.ai)".

Yejin Choi (born 1977) is a South Korean-American computer scientist and the Dieter Schwarz Foundation HAI Professor and Professor of Computer Science at [Stanford University](/wiki/stanford_university), where she is also a Senior Fellow at the Stanford Institute for Human-Centered Artificial Intelligence (Stanford HAI).[^1][^2] She is best known for shaping the modern research agenda on commonsense reasoning, social and moral reasoning, and constrained generation in [natural language processing](/wiki/natural_language_processing), and for advocating that smaller, knowledge-rich language models can rival much larger systems trained at greater compute.[^1][^3][^4]

Choi's work has been cited more than 99,000 times, giving her an h-index of 141, among the highest of any researcher in natural language processing, and she is a co-inventor of nucleus sampling (top-p), the decoding technique introduced in her 2020 paper "The Curious Case of Neural Text Degeneration" that became a default setting for generating text from [large language model](/wiki/large_language_model)s.[^40][^41]

Before joining Stanford in 2025, Choi spent a decade on the faculty of the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where she held the Brett Helsel Career Development Professorship and, in 2023, the Wissner-Slivka Chair, with a joint appointment as Senior Research Manager at the [Allen Institute for AI](/wiki/allen_institute_for_ai) (also known as [AI2](/wiki/ai2)).[^3][^5][^6] In August 2024 she left both UW and AI2 to move to California, joining [NVIDIA](/wiki/nvidia) as Senior Director of AI Research before transitioning to Stanford the following year.[^7][^2]

Choi was named a MacArthur Fellow in 2022 for using natural language processing to develop AI systems capable of commonsense reasoning, and she has been named to TIME's list of the 100 most influential people in AI in both 2023 and 2025.[^8][^9][^4] Her 2023 TED talk, "Why AI is incredibly smart and shockingly stupid," has become one of the most widely cited public explanations of the limits of large language models.[^10][^11]

## Key facts

| | |
|---|---|
| Born | 1977, South Korea[^12] |
| Education | BS, Seoul National University (1999); PhD in Computer Science, Cornell University (2010, advised by Claire Cardie)[^8][^13][^12] |
| Current position | Dieter Schwarz Foundation HAI Professor and Professor of Computer Science, [Stanford University](/wiki/stanford_university); Senior Fellow, Stanford HAI (2025-present)[^1][^2] |
| Previous positions | Assistant Professor, SUNY Stony Brook (2010-2014); Assistant to Full Professor, University of Washington (2014-2024); Senior Research Manager, [Allen Institute for AI](/wiki/allen_institute_for_ai) (2018-2024); Senior Director of AI Research, [NVIDIA](/wiki/nvidia) (2024-2025)[^12][^5][^7] |
| Best-known projects | Nucleus sampling (top-p), ATOMIC, COMET, Delphi, Social IQa, Social Chemistry 101, [HellaSwag](/wiki/hellaswag), [WinoGrande](/wiki/winogrande), PIQA, [ToxiGen](/wiki/toxigen) (collaborator), NeuroLogic Decoding, Impossible Distillation, Artificial Hivemind[^41][^14][^15][^16][^17][^18][^19][^20][^21][^22][^44][^35] |
| Citation impact | More than 99,000 citations; h-index 141; i10-index 410 (Google Scholar, 2026)[^40] |
| Notable honors | MacArthur Fellowship (2022); ACL Fellow (2022); AI2050 Senior Fellow (2024); TIME100 AI (2023, 2025); ACL Test-of-Time Award (2021); [CVPR](/wiki/cvpr) Longuet-Higgins Prize (2021); [ICCV](/wiki/iccv) Marr Prize (2013); Borg Early Career Award (2018); IEEE AI's 10 to Watch (2016); [NeurIPS](/wiki/neurips) Best Paper Award (2025)[^8][^4][^23][^24][^35] |
| Public profile | TED 2023 main-stage speaker; UN Security Council expert briefer (2025); subject of features in *The New York Times*, NPR, GeekWire, and *Time*[^10][^42][^25][^26] |

## Early life and education

Yejin Choi was born in 1977 in South Korea and grew up there before moving to the United States for graduate study.[^12] She earned a Bachelor of Science in computer science and engineering from Seoul National University in 1999, then enrolled at Cornell University in upstate New York for her doctorate.[^8][^12]

At Cornell, Choi worked with the natural language processing researcher Claire Cardie. Her dissertation, "Fine-Grained Opinion Analysis: Structure-Aware Approaches," focused on extracting opinions and their sources from text using structured prediction methods.[^13][^12] Among the papers from her doctoral period, "Identifying sources of opinions with conditional random fields and extraction patterns," co-authored with Cardie and others in 2005, became an early standard reference in opinion extraction.[^12] She received her PhD in computer science from Cornell in 2010.[^8][^13]

## Career at Stony Brook, the University of Washington, and AI2

After completing her PhD, Choi joined the State University of New York at Stony Brook as an assistant professor of computer science. Her early work at Stony Brook, between 2010 and 2014, applied NLP to detect deception in online text. The paper "Finding Deceptive Opinion Spam by Any Stretch of the Imagination," presented in 2011, used computational linguistics to distinguish fake from authentic hotel reviews and helped launch a strand of research on text-based deception detection.[^12]

In 2014, Choi moved across the country to the Paul G. Allen School of Computer Science & Engineering at the University of Washington in Seattle, where she rose through the ranks to full professor.[^5] At UW she helped lead a UW Natural Language Processing group that during the late 2010s and early 2020s produced some of the most widely used commonsense reasoning benchmarks and language-generation methods in the field.

In 2017, Choi was a faculty advisor for the UW team Sounding Board, a socialbot that won the inaugural Amazon Alexa Prize, beating teams from more than twenty countries to a US$500,000 first-place award.[^27]

She joined the [Allen Institute for AI](/wiki/allen_institute_for_ai), the Seattle-based research nonprofit founded by Microsoft co-founder Paul Allen, in 2018, eventually serving as senior research manager and leading the AI2 Mosaic team, the institute's group focused on commonsense reasoning and language understanding.[^12][^28]

At UW, Choi held the Brett Helsel Career Development Professorship from 2020 to 2023 and was named to the endowed Wissner-Slivka Chair in 2023.[^6][^29] Over the course of her decade in Seattle she received an exceptionally large concentration of "best paper" and "outstanding paper" recognitions at top venues, including AAAI 2020, NeurIPS 2021, the ACL Test-of-Time Award in 2021, the [CVPR](/wiki/cvpr) Longuet-Higgins Prize in 2021, NAACL Best Paper in 2022, ICML Outstanding Paper in 2022, ACL Best Paper in 2023, and EMNLP Outstanding Paper in 2023.[^4][^29]

In August 2024 Choi announced internally at AI2 and the Allen School that she was leaving both organizations. "Even though everything is so wonderful here, I am adventure-seeking by nature," she wrote to UW colleagues, indicating that she planned to move to California, with a new role to be confirmed.[^7] Her next position was as Senior Director of AI Research at [NVIDIA](/wiki/nvidia), based in the company's research organization.[^7][^2]

## Where does Yejin Choi work now? The move to Stanford (2025)

On 16 January 2025, Stanford HAI announced that Choi would join the institute as the inaugural Dieter Schwarz Foundation HAI Professor, with a full professorship in the Stanford Department of Computer Science and a Senior Fellowship at Stanford HAI.[^2] "Yejin is a pioneer in common sense AI and her work is reshaping the field," said Stanford HAI co-director James Landay in the announcement, which positioned the appointment as central to the institute's research agenda on aligning AI with societal values, with a particular emphasis on Choi's ongoing work on common-sense reasoning and on the shift from large language models to smaller, more efficient language models.[^2][^1]

The Dieter Schwarz Foundation chair at Stanford is an endowed professorship funded by the German Dieter Schwarz Foundation; Choi is the inaugural holder of the position at Stanford HAI.[^2] Her Stanford HAI profile describes her research as focused on "democratizing generative AI through smaller yet powerful language models, scaling intelligence via smarter algorithms, pluralistic alignment, and AI for science and social good," and lists themes including test-time training, reinforced pretraining, theory of mind, and molecular and protein foundation models.[^1] Following her move, she also became an affiliate faculty member of the Allen School at the University of Washington, maintaining her ties to the Seattle NLP community.[^35]

## Research themes

### What is commonsense reasoning, and how did Choi advance it?

A central focus of Choi's career has been giving AI systems "common sense," the kind of implicit world knowledge that humans deploy effortlessly but which had long eluded NLP systems.[^8] She has argued that purely scaling up language models on web data is insufficient, because commonsense knowledge is often unstated in text and is interwoven with norms and values that web data does not represent reliably.[^11]

In 2019 Choi was senior author on **ATOMIC** ("An Atlas of Machine Commonsense for If-Then Reasoning"), a knowledge graph of approximately 877,000 textual descriptions of inferential, causal, and social commonsense relations (e.g., if X pays Y a compliment, Y will likely return the compliment), structured around nine if-then relation types.[^14] ATOMIC was followed by **COMET** (Commonsense Transformers), introduced at ACL 2019, in which a neural language model was trained on commonsense knowledge graphs so that it could *generate* commonsense inferences for previously unseen situations rather than retrieve them, with human judges rating more than 75% of its outputs as plausible.[^8][^14] A 2021 successor, COMET-ATOMIC 2020, scaled and combined symbolic and neural commonsense knowledge graphs.[^30]

Choi was also a co-author of several other widely used commonsense and reasoning benchmarks created during her UW/AI2 years, including [HellaSwag](/wiki/hellaswag) (Zellers, Holtzman, Bisk, Farhadi, and Choi, ACL 2019), a benchmark of "adversarial" sentence-completion problems that became a standard evaluation for large language models; [WinoGrande](/wiki/winogrande) (Sakaguchi, Le Bras, Bhagavatula, and Choi, AAAI 2020), a large-scale Winograd Schema challenge that won an AAAI 2020 Outstanding Paper Award; **PIQA** (Bisk, Zellers, Le Bras, Gao, and Choi, AAAI 2020), a benchmark for physical commonsense reasoning; and Social IQa (Sap, Rashkin, Chen, Le Bras, and Choi, EMNLP-IJCNLP 2019), the first large-scale benchmark for commonsense reasoning about everyday social situations.[^15][^16][^31][^44][^17] These four benchmarks became so standard that they now appear routinely in the evaluation tables of frontier language model releases.[^40]

The CVPR 2021 Longuet-Higgins Prize, awarded retroactively to her 2013 work on generating image descriptions ("BabyTalk"), recognized her earlier influential contributions at the boundary of language and vision.[^12][^4]

### Social, ethical, and moral reasoning: Delphi, Social Chemistry, ToxiGen

Starting around 2020, Choi's group extended commonsense modeling into the moral and social domain. **Social Chemistry 101** (Forbes, Hwang, Shwartz, Sap, and Choi, EMNLP 2020) introduced a 292,000-entry corpus of "rules of thumb," everyday social and moral norms such as "It is rude to run a blender at 5 a.m.," annotated along twelve dimensions of human judgment.[^19] The Neural Norm Transformer model trained on Social Chemistry 101 could generalize to previously unseen situations.[^19]

In October 2021, Choi's team at AI2 released **Delphi**, an experimental neural system designed to make moral judgments about everyday scenarios.[^25][^32] The accompanying paper, "Can Machines Learn Morality? The Delphi Experiment," reported that human judges rated Delphi's ethical judgments as up to 92% accurate.[^32] Delphi became one of the most heavily discussed AI experiments of its time: it drew substantial press coverage in *The New York Times*, NPR, and elsewhere, but also significant academic criticism from scholars who questioned both the framing of "machine morality" and the methodology of treating ethics as a labeled-data classification problem.[^25][^32] After early complaints, the project relabeled Delphi as "a research prototype designed to model people's moral judgments" rather than an oracle that "says" what is right or wrong.[^25] An updated, peer-reviewed account of the experiment subsequently appeared in *Nature Machine Intelligence*.[^33]

In a related strand, [ToxiGen](/wiki/toxigen) (Hartvigsen, Gabriel, Palangi, Sap, Ray, and Kamar, ACL 2022), on which Choi's longtime collaborators Sap and Gabriel were authors, introduced a large machine-generated dataset for adversarial and implicit hate-speech detection covering thirteen minority identity groups, and helped catalyse a line of work in Choi's broader network on detoxifying language models.[^34]

### How does Choi's work shape the way language models generate text?

Choi's group has been central to research on the *decoding* stage of generation, the step that turns a model's probability distribution into actual words. Her single most-cited paper, "The Curious Case of Neural Text Degeneration" (Holtzman, Buys, Du, Forbes, and Choi, ICLR 2020), showed that maximization-based decoding such as beam search produces text that is bland, incoherent, or gets stuck in repetitive loops, and introduced **nucleus sampling** (also called top-p sampling), which draws the next token only from the smallest set of candidates whose cumulative probability exceeds a threshold p.[^41] Nucleus sampling became one of the most widely adopted decoding methods in the field, cited roughly 5,300 times by 2026, and remains a default text-generation setting across large language models.[^41][^40]

**NeuroLogic Decoding** (Lu, West, Zellers, Le Bras, Bhagavatula, and Choi, NAACL 2021) introduced a beam-search algorithm that lets a neural language model satisfy arbitrary predicate-logic constraints on its output without retraining the underlying model.[^20] Follow-up work, including NeuroLogic A*esque decoding, extended this with lookahead heuristics for constrained generation.[^20]

This decoding line is closely tied to Choi's interest in what she has called pluralistic alignment: methods and data designed to reflect a plurality of human values rather than collapse to a single voice. Her Stanford profile lists pluralistic alignment, theory of mind, and "alternative training methods" such as test-time training and reinforced pretraining among her current research themes.[^1]

### Can small models beat big compute?

A recurring theme of Choi's recent talks and papers is the argument that knowledge- and algorithm-rich smaller models can compete with much larger ones. **Impossible Distillation** (Jung, West, Jiang, Brahman, Lu, Fisher, Sorensen, and Choi, NAACL 2024) showed that a 770-million-parameter student model trained on a dataset distilled from a smaller and "low-quality" teacher language model could outperform the 175-billion-parameter GPT-3 on summarization and paraphrasing tasks, as judged by both automatic metrics and human evaluators.[^21][^22] The work also released **DIMSUM+**, a dataset of 3.4 million machine-generated summaries and paraphrases.[^22] Choi's Stanford HAI announcement explicitly described her continuing work on "the shift from LLMs to SLMs" (large to small language models) as a central reason for her recruitment.[^2]

In late 2025, a paper on which Choi was the senior author, "Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)," led by Allen School PhD student Liwei Jiang, received one of four Best Paper Awards at [NeurIPS](/wiki/neurips) 2025, in the Datasets and Benchmarks track.[^35][^45] Using Infinity-Chat, a benchmark of 26,000 real-world open-ended queries paired with 31,250 human annotations (25 independent judgments per example), the team tested more than 70 leading [large language model](/wiki/large_language_model)s and found that they exhibit both "intra-model" repetition and pronounced "inter-model" homogeneity, converging on strikingly similar responses, an "artificial hivemind" effect with implications for diversity, safety, and information ecology.[^35][^36] Co-author Yulia Tsvetkov summarized the finding: "Testing over 70 models from major AI developers, our study found systematic convergence on similar responses to open-ended queries."[^35] The paper sits at the intersection of several of her enduring concerns, including evaluation beyond multiple-choice benchmarks, the role of training data and reinforcement learning in homogenizing model outputs, and the relationship between scale and genuine reasoning ability, and was published shortly after her transition to Stanford.[^35][^36]

Choi's group has also explored cultural [natural language processing](/wiki/natural_language_processing) and the cross-cultural transfer of social norms, theory-of-mind benchmarks for language models, and the use of [large language model](/wiki/large_language_model) outputs as scaffolds for scientific reasoning, particularly in molecular and protein domains. These efforts are reflected in her current Stanford research portfolio and in recent invited talks at venues such as the NSF CISE Distinguished Lecture series and the Hans J. Berliner Lecture in AI at Carnegie Mellon.[^1][^38][^39]

## How influential is Yejin Choi's research?

By 2026, Choi's publications had accumulated more than 99,000 citations, with an h-index of 141 and an i10-index of 410, placing her among the most-cited researchers in natural language processing.[^40] Several of her group's datasets and methods have become standard fixtures in the training and evaluation of large language models. Her most-cited works span decoding methods, commonsense benchmarks, and multimodal evaluation.

| Paper | Year | Contribution | Citations (2026) |
|---|---|---|---|
| The Curious Case of Neural Text Degeneration | 2020 | Nucleus (top-p) sampling | ~5,300[^40] |
| [HellaSwag](/wiki/hellaswag) | 2019 | Adversarial sentence-completion benchmark | ~4,800[^40] |
| [WinoGrande](/wiki/winogrande) | 2020 | Large-scale Winograd schema challenge | ~3,700[^40] |
| PIQA | 2020 | Physical commonsense QA benchmark | ~3,300[^40] |
| CLIPScore | 2021 | Reference-free image-captioning metric | ~3,200[^40] |

## Awards and honors

- **MacArthur Fellowship** (2022), the so-called "genius grant," awarded for her work on natural language processing systems capable of [commonsense reasoning](/wiki/commonsense_reasoning). The fellowship came with an unrestricted US$800,000 stipend.[^8][^26]
- **ACL Fellow** (2022), recognizing her sustained contributions to [natural language processing](/wiki/natural_language_processing).[^11]
- **AI2050 Senior Fellow** (2024), under Schmidt Sciences' AI2050 initiative.[^23]
- **TIME 100 AI** (2023 and 2025): named to *Time* magazine's annual list of the 100 most influential people in artificial intelligence.[^9][^4][^1]
- **ACL Test-of-Time Award** (2021) and **[CVPR](/wiki/cvpr) Longuet-Higgins Prize** (2021), recognizing influential earlier work.[^29][^12]
- **[ICCV](/wiki/iccv) Marr Prize** (2013), one of computer vision's most prestigious paper awards.[^29][^12]
- **Borg Early Career Award** (2018), honoring early-career women in computing research.[^24]
- **IEEE AI's 10 to Watch** (2016) and the Anita Borg-related Borg Early Career Award (2018).[^29][^24]
- Numerous best and outstanding paper awards at top venues including AAAI 2020, [NeurIPS](/wiki/neurips) 2021, NAACL 2022, ICML 2022, ACL 2023, EMNLP 2023, ACL 2025 (Best Demo Paper and an Outstanding Paper), EMNLP 2025 (Best Paper), the [NeurIPS](/wiki/neurips) 2025 Best Paper Award, and a CVPR 2026 Best Paper Honorable Mention.[^29][^35][^4]

By 2026 her Stanford and Stanford HAI profiles described her as a co-recipient of two Test-of-Time Awards and ten Best and Outstanding Paper Awards at top AI conferences.[^1][^29]

## Public profile and policy engagement

Choi has become one of the most visible NLP researchers in the public conversation about AI. In April 2023 she delivered a TED main-stage talk, "Why AI is incredibly smart and shockingly stupid," which used vivid examples of large language models failing at basic commonsense reasoning to argue that scaling alone will not produce reliable intelligence, and to make the case for AI systems grounded in human norms and values.[^10][^11] The talk highlighted three problems with cutting-edge large language models, illustrated with examples of state-of-the-art systems failing on simple physical and social reasoning, and concluded that smaller AI systems trained on human norms and values offer a more promising path than indefinitely scaling raw web-data training.[^10]

Throughout the public-facing arc of her work she has tended to frame AI capabilities and limits in terms that emphasize the gap between what large models can pattern-match and what they actually understand. In interviews tied to the MacArthur Fellowship and to *Time*'s AI lists, she has repeatedly stressed that superior performance on one axis, such as text generation fluency, does not equate to general intelligence, and has argued for the policy importance of teaching AI systems social and moral norms specifically because of their relevance to alignment with human values.[^9][^26]

She has been featured in *Time* magazine's TIME 100 AI list in 2023 and 2025, with the 2023 profile describing her as a researcher "focused on the many ways in which human intelligence differs from that of AIs like ChatGPT" and noting that she had begun working on AI systems that understand social and moral norms specifically because of their relevance to alignment.[^9] She has also been profiled in *The New York Times*, NPR's *Short Wave*, *Gizmodo*, and GeekWire, often in connection with Delphi and commonsense reasoning.[^25][^37][^26][^7]

### What did Yejin Choi tell the UN Security Council?

On 24 September 2025, Choi served as an invited expert briefer at a high-level United Nations Security Council open debate on artificial intelligence, held under the "Maintenance of International Peace and Security" agenda and chaired by Republic of Korea President Lee Jae Myung.[^42][^43] Reprising her case for efficiency and access, she warned that the benefits of AI had become too concentrated among a few companies and countries: "When only a few have the resources to build and benefit from AI, we leave the rest of the world waiting at the door."[^43] She urged governments and international institutions to invest in alternatives to ever-larger models, pressed for stronger representation of linguistic and cultural diversity in leading systems that she said underperform for many non-English languages, and called on the global scientific and policy communities to pursue intelligence "that is not only powerful, but also accessible, robust, and efficient."[^42][^43] She closed by urging the council to "expand what intelligence can be, and let everyone everywhere have a role in building it."[^43]

Choi has delivered keynote and distinguished lectures at major venues including AAAI, ACL, [CVPR](/wiki/cvpr), ICLR, MLSys, VLDB, WebConf, the AAMAS 2023 keynote programme, the NSF CISE Distinguished Lecture, and the Hans J. Berliner Lecture in Artificial Intelligence at Carnegie Mellon (2025).[^1][^38][^39]

There is no reliably reported record of Choi having founded or co-founded a company outside academia; her industry experience to date consists of her tenure at [NVIDIA](/wiki/nvidia) Research as Senior Director of AI Research between 2024 and 2025 and her ongoing research at [Stanford University](/wiki/stanford_university) and Stanford HAI.[^7][^2][^1]

## Selected publications

- Yejin Choi, *Fine-Grained Opinion Analysis: Structure-Aware Approaches* (PhD dissertation, Cornell University, 2010).[^13]
- Y. Choi, C. Cardie, E. Riloff, and S. Patwardhan, "Identifying sources of opinions with conditional random fields and extraction patterns," HLT/EMNLP 2005.[^12]
- M. Ott, Y. Choi, C. Cardie, and J. T. Hancock, "Finding deceptive opinion spam by any stretch of the imagination," ACL 2011.[^12]
- G. Kulkarni, V. Premraj, S. Dhar, S. Li, Y. Choi, A. C. Berg, and T. L. Berg, "BabyTalk: Understanding and generating simple image descriptions," IEEE Transactions on Pattern Analysis and Machine Intelligence 35(12) (2013).[^12]
- M. Sap, R. Le Bras, E. Allaway, C. Bhagavatula, N. Lourie, H. Rashkin, B. Roof, N. A. Smith, and Y. Choi, "ATOMIC: An atlas of machine commonsense for if-then reasoning," AAAI 2019.[^14]
- A. Bosselut, H. Rashkin, M. Sap, C. Malaviya, A. Celikyilmaz, and Y. Choi, "COMET: Commonsense Transformers for automatic knowledge graph construction," ACL 2019.[^14]
- R. Zellers, A. Holtzman, Y. Bisk, A. Farhadi, and Y. Choi, "HellaSwag: Can a machine really finish your sentence?," ACL 2019.[^15]
- A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi, "The Curious Case of Neural Text Degeneration," ICLR 2020.[^41]
- M. Sap, H. Rashkin, D. Chen, R. Le Bras, and Y. Choi, "Social IQa: Commonsense reasoning about social interactions," EMNLP-IJCNLP 2019.[^17]
- Y. Bisk, R. Zellers, R. Le Bras, J. Gao, and Y. Choi, "PIQA: Reasoning about physical commonsense in natural language," AAAI 2020.[^44]
- K. Sakaguchi, R. Le Bras, C. Bhagavatula, and Y. Choi, "WinoGrande: An adversarial Winograd schema challenge at scale," AAAI 2020 (Outstanding Paper).[^16][^31]
- M. Forbes, J. D. Hwang, V. Shwartz, M. Sap, and Y. Choi, "Social Chemistry 101: Learning to reason about social and moral norms," EMNLP 2020.[^19]
- X. Lu, P. West, R. Zellers, R. Le Bras, C. Bhagavatula, and Y. Choi, "NeuroLogic Decoding: (Un)supervised neural text generation with predicate logic constraints," NAACL 2021.[^20]
- J. D. Hwang, C. Bhagavatula, R. Le Bras, J. Da, K. Sakaguchi, A. Bosselut, and Y. Choi, "COMET-ATOMIC 2020: On symbolic and neural commonsense knowledge graphs," AAAI 2021.[^30]
- L. Jiang, J. D. Hwang, C. Bhagavatula, R. Le Bras, M. Forbes, J. Borchardt, J. Liang, O. Etzioni, M. Sap, and Y. Choi, "Can machines learn morality? The Delphi experiment," 2021/2024.[^32][^33]
- J. Jung, P. West, L. Jiang, F. Brahman, X. Lu, J. Fisher, T. Sorensen, and Y. Choi, "Impossible Distillation: From low-quality model to high-quality dataset & model for summarization and paraphrasing," NAACL 2024.[^21][^22]
- L. Jiang, Y. Chai, M. Li, M. Liu, R. Fok, N. Dziri, Y. Tsvetkov, M. Sap, and Y. Choi, "Artificial Hivemind: The open-ended homogeneity of language models (and beyond)," [NeurIPS](/wiki/neurips) 2025 (Best Paper Award, Datasets and Benchmarks track).[^35][^36][^45]

## References

[^1]: "Yejin Choi," Stanford HAI, https://hai.stanford.edu/people/yejin-choi (accessed 2026).
[^2]: "NVIDIA's Yejin Choi Joins Stanford HAI," Stanford HAI press release, 16 January 2025, https://hai.stanford.edu/news/nvidias-yejin-choi-joins-stanford-hai; reposted at https://www.businesswire.com/news/home/20250116228686/en/NVIDIAs-Yejin-Choi-Joins-Stanford-HAI.
[^3]: "Yejin Choi: Teaching AI How the World Works," Stanford HAI, https://hai.stanford.edu/news/yejin-choi-teaching-ai-how-world-works.
[^4]: "Yejin Choi," personal website, https://yejinc.github.io/.
[^5]: "Yejin Choi," Wikipedia, https://en.wikipedia.org/wiki/Yejin_Choi (accessed 2026).
[^6]: "The Wissner-Slivka Chair," Paul G. Allen School of Computer Science & Engineering, https://www.cs.washington.edu/supportcse/faculty/wissner-slivka_chair.
[^7]: T. Bishop, "Tech Moves: AI researcher Yejin Choi leaves Univ. of Washington and Allen Institute for AI," GeekWire, August 2024, https://www.geekwire.com/2024/tech-moves-ai-researcher-yejin-choi-leaving-university-of-washington-and-allen-institute-for-ai/.
[^8]: "Yejin Choi," MacArthur Foundation, Class of 2022, https://www.macfound.org/fellows/class-of-2022/yejin-choi.
[^9]: "Yejin Choi: The 100 Most Influential People in AI 2023," *Time*, https://time.com/collections/time100-ai/6311114/yejin-choi-2/.
[^10]: Y. Choi, "Why AI is incredibly smart and shockingly stupid," TED, April 2023, https://www.ted.com/talks/yejin_choi_why_ai_is_incredibly_smart_and_shockingly_stupid.
[^11]: "Yejin Choi," TED speaker page, https://www.ted.com/speakers/yejin_choi.
[^12]: "Yejin Choi," Wikipedia, https://en.wikipedia.org/wiki/Yejin_Choi.
[^13]: "Choi '10 CS Ph.D. named 2022 MacArthur Fellow," Cornell Bowers CIS, https://bowers.cornell.edu/choi-%E2%80%9810-cs-phd-named-2022-macarthur-fellow.
[^14]: M. Sap et al., "ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning," AAAI 2019, https://arxiv.org/abs/1811.00146; A. Bosselut et al., "COMET: Commonsense Transformers for Automatic Knowledge Graph Construction," ACL 2019, https://aclanthology.org/P19-1470/.
[^15]: R. Zellers, A. Holtzman, Y. Bisk, A. Farhadi, Y. Choi, "HellaSwag: Can a Machine Really Finish Your Sentence?," ACL 2019, https://aclanthology.org/P19-1472/.
[^16]: K. Sakaguchi, R. Le Bras, C. Bhagavatula, Y. Choi, "WinoGrande: An Adversarial Winograd Schema Challenge at Scale," AAAI 2020, https://ojs.aaai.org/index.php/AAAI/article/view/6399.
[^17]: M. Sap, H. Rashkin, D. Chen, R. Le Bras, Y. Choi, "Social IQa: Commonsense Reasoning about Social Interactions," EMNLP-IJCNLP 2019, https://aclanthology.org/D19-1454/.
[^18]: T. Hartvigsen, S. Gabriel, H. Palangi, M. Sap, D. Ray, E. Kamar, "ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection," ACL 2022, https://aclanthology.org/2022.acl-long.234.pdf.
[^19]: M. Forbes, J. D. Hwang, V. Shwartz, M. Sap, Y. Choi, "Social Chemistry 101: Learning to Reason about Social and Moral Norms," EMNLP 2020, https://aclanthology.org/2020.emnlp-main.48/.
[^20]: X. Lu, P. West, R. Zellers, R. Le Bras, C. Bhagavatula, Y. Choi, "NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints," NAACL 2021, https://aclanthology.org/2021.naacl-main.339/.
[^21]: J. Jung et al., "Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing," arXiv:2305.16635, https://arxiv.org/abs/2305.16635.
[^22]: J. Jung et al., "Impossible Distillation for Paraphrasing and Summarization: How to Make High-quality Lemonade out of Small, Low-quality Models," NAACL 2024, https://aclanthology.org/2024.naacl-long.250/.
[^23]: "Yejin Choi," AI2050 Fellows, Schmidt Sciences, https://ai2050.schmidtsciences.org/fellow/yejin-choi/.
[^24]: "Meet the 2018 BECA Winners - Yejin Choi and Reetuparna Das," Computing Research Association, https://cra.org/meet-the-2018-beca-winners-yejin-choi-and-reetuparna-das/.
[^25]: C. Metz, "Can a Machine Learn Morality?," *The New York Times*, reprinted at *The Seattle Times*, November 2021, https://www.seattletimes.com/business/can-a-machine-learn-morality-seattle-researchers-hope-so/.
[^26]: T. Bishop, "University of Washington computer science professor Yejin Choi wins $800K 'genius grant'," GeekWire, October 2022, https://www.geekwire.com/2022/university-of-washington-computer-science-professor-yejin-choi-wins-800k-genius-grant/.
[^27]: "UW's Sounding Board wins inaugural Amazon Alexa Prize," Allen School News, 28 November 2017, https://news.cs.washington.edu/2017/11/28/uws-sounding-board-wins-inaugural-amazon-alexa-prize/.
[^28]: "Yejin Choi," AI2 (Allen Institute for AI), https://allenai.org/team/yejinc.
[^29]: "'Go ahead and take that adventurous route': Allen School professor Yejin Choi named 2022 MacArthur Fellow," Allen School News, 12 October 2022, https://news.cs.washington.edu/2022/10/12/go-ahead-and-take-that-adventurous-route-allen-school-professor-yejin-choi-named-2022-macarthur-fellow/.
[^30]: J. D. Hwang et al., "(COMET-)ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs," AAAI 2021, https://ojs.aaai.org/index.php/AAAI/article/view/16792/16599.
[^31]: "Allen School and AI2 researchers earn Outstanding Paper Award at AAAI for advancing new techniques for testing natural language understanding," Allen School News, 3 March 2020, https://news.cs.washington.edu/2020/03/03/allen-school-and-ai2-researchers-earn-outstanding-paper-award-at-aaai-for-advancing-new-techniques-for-testing-natural-language-understanding/.
[^32]: L. Jiang et al., "Can Machines Learn Morality? The Delphi Experiment," arXiv:2110.07574, https://arxiv.org/abs/2110.07574.
[^33]: L. Jiang et al., "Investigating machine moral judgement through the Delphi experiment," *Nature Machine Intelligence*, 2024, https://www.nature.com/articles/s42256-024-00969-6.
[^34]: T. Hartvigsen et al., "ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection," ACL 2022 (long paper 234), https://aclanthology.org/2022.acl-long.234.pdf.
[^35]: "Allen School researchers earn NeurIPS Best Paper Award for Artificial Hivemind Effect across LLM Open-Ended Generation," Allen School News, 22 January 2026, https://news.cs.washington.edu/2026/01/22/allen-school-researchers-earn-neurips-best-paper-award-for-artificial-hivemind-effect-across-llm-open-ended-generation/.
[^36]: "Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)," NeurIPS 2025 poster, https://neurips.cc/virtual/2025/poster/121421.
[^37]: "How MacArthur Fellow Yejin Choi teaches common sense to artificial intelligence," NPR *Short Wave*, 30 January 2023, https://www.npr.org/2023/01/30/1152568554/can-you-teach-a-computer-common-sense.
[^38]: "Keynote Speakers," AAMAS 2023, https://aamas2023.soton.ac.uk/program/keynote-speakers/.
[^39]: "Hans J. Berliner Lecture in Artificial Intelligence - Yejin Choi," Carnegie Mellon Computer Science Department, 4 September 2025, https://www.csd.cs.cmu.edu/calendar/2025-09-04/hans-j-berliner-lecture-in-artificial-intelligence-yejin-choi.
[^40]: "Yejin Choi," Google Scholar (citation metrics as of 2026), https://scholar.google.com/citations?user=vhP-tlcAAAAJ.
[^41]: A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi, "The Curious Case of Neural Text Degeneration," ICLR 2020, arXiv:1904.09751, https://arxiv.org/abs/1904.09751.
[^42]: "Yejin Choi's Briefing to the United Nations Security Council," Stanford HAI, 24 September 2025, https://hai.stanford.edu/policy/yejin-chois-briefing-to-the-united-nations-security-council.
[^43]: "AI must not decide humanity's fate, UN chief warns Security Council," UN News, 24 September 2025, https://news.un.org/en/story/2025/09/1165942.
[^44]: Y. Bisk, R. Zellers, R. Le Bras, J. Gao, Y. Choi, "PIQA: Reasoning about Physical Commonsense in Natural Language," AAAI 2020, https://ojs.aaai.org/index.php/AAAI/article/view/6239.
[^45]: "Announcing the NeurIPS 2025 Best Paper Awards," NeurIPS Blog, 26 November 2025, https://blog.neurips.cc/2025/11/26/announcing-the-neurips-2025-best-paper-awards/.