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Taiwei Shi

Taiwei Shi has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining; often with Caleb Ziems, Diyi Yang, Jieyu Zhao.

2 papers in our library · 2023–2026

OpenAlex · h-index 3 · 16 works · 58 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 63 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #6694

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

🎓University of Southern CaliforniaLos Angeles · United States2023–2026 · 7 paperscurrent🎓University of California San DiegoSan Diego · United States2026 · 1 papercurrent🎓University of MinnesotaMinneapolis · United States2026 · 1 papercurrent🎓University of Wisconsin SystemMadison · United States2026 · 1 papercurrent🎓University of Wisconsin–MadisonMadison · United States2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksGeneral Social Sciences · 1 workSocial Sciences · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 2 works

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation (2023 · 101 citations)
    Large language models such as ChatGPT can label text at zero-shot, cheaply and at scale. Earlier studies usually treated them as replacements for human annotators. This paper asks a different question: within one dataset, which items should go to the model and which to people? The proposed framework, CoAnnotating…
  2. WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026 · 9 citations)
    Aligning LLMs with human preferences usually depends on curated preference data. Human annotation is expensive and subjective, and GPT-4-labelled synthetic data can reinforce a model's own biases. WildFeedback instead builds preference data from feedback that real users give naturally while chatting with a model. The…

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data AnnotationConference on Empirical Methods in Natural Language Processing (EMNLP)Minzhi Li, Taiwei Shi, Caleb Ziems, et al.2023 · 101 citations
  2. WildFeedback: Aligning LLMs With In-situ User Interactions And FeedbackAnnual Meeting of the Association for Computational Linguistics (ACL)Taiwei Shi, Zhuoer Wang, Longqi Yang, et al.2026 · 9 citations
  3. Video-Based Reward Modeling for Computer-Use Agents ↗Lecture notes in computer science · no summary yetLinxin Song et al.2026
  4. DP-RFT: Learning to Generate Synthetic Text via Differentially Private Reinforcement Fine-Tuning ↗arXiv (Cornell University) · no summary yetFangyuan Xu et al.2026

 Co-authors here

11 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 63 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

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