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Cinoo Lee

Cinoo Lee has 3 papers in our library, in 2023–2025, most of them on Language models & pretraining; often with Dan Jurafsky, Emmanuel J. Candès, Esin Durmus.

3 papers in our library · 2023–2025

OpenAlex · ORCID · h-index 5 · 13 works · 210 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 374 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #3396

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Stanford UniversityStanford · United States2022–2026 · 8 papersEmploymentcurrent🏢MicrosoftRedmond · United States2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 9The field of science this work belongs to (OpenAlex fields).Social Sciences · 2 worksSafety Research · 2 worksArtificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksPsychology · 1 workSocial Psychology · 1 workSociology and Political Science · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3 works

 Works by year

2020s

20252 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Whose Opinions Do Language Models Reflect? (2023 · 414 citations)
    Language models increasingly answer subjective questions that have no correct answer, so any reply, including a refusal, takes a stance.
  2. ELEPHANT: Measuring and understanding social sycophancy in LLMs (2025 · 188 citations)
    Earlier sycophancy research checks whether an LLM drops a factual answer to agree with a belief the user states outright.
  3. Can Unconfident LLM Annotations Be Used for Confident Conclusions? (2025 · 19 citations)
    Computational social scientists increasingly add LLM annotations to costly human labels. However, LLM errors and biases can quietly distort downstream statistics such as prevalences or regression coefficients. The authors propose CONFIDENCE-DRIVEN INFERENCE, which asks an LLM for both a label and a verbalized…

All works

6 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Whose Opinions Do Language Models Reflect?arXiv:2303.17548Shibani Santurkar, Esin Durmus, Faisal Ladhak, et al.2023 · 414 citations
  2. ELEPHANT: Measuring and understanding social sycophancy in LLMsScienceMyra Cheng, Sunny Yu, Cinoo Lee, et al.2025 · 188 citations
  3. Can Unconfident LLM Annotations Be Used for Confident Conclusions?Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)Kristina Gligorić, Tijana Zrnic, Cinoo Lee, et al.2025 · 19 citations
  4. Sycophantic AI makes human interaction feel more effortful and less satisfying over time ↗arXiv (Cornell University) · no summary yetLujain Ibrahim et al.2026 · 6 citations
  5. Social calibration of sycophantic AI—Response ↗Science · no summary yetMyra Cheng et al.2026
  6. Warning labels shift perceptions of sycophantic AI, but not its influence ↗arXiv (Cornell University) · no summary yetLujain Ibrahim et al.2026

 Co-authors here

10 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 374 times (of the 149,439 papers whose bibliographies we read); 104 citing papers with a page here →.

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