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Yatao Bian

Yatao Bian has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Bias, fairness & safety; often with Bingzhe Wu, Hongru Sun, Jun Xu.

2 papers in our library · 2023–2026

OpenAlex · ORCID · Wikidata · h-index 19 · 100 works · 1,440 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 73 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #8679

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

🎓National University of SingaporeSingapore · Singapore2023–2026 · 33 papersEmployment since 2025 · Assistant Professor, Computer Sciencecurrent🏢TencentShenzhen · China2020–2025 · 49 papers🎓ETH ZurichZurich · Switzerland2013–2019 · 10 papersEmployment · Research Assistant, Computer Science🎓Chinese University of Hong KongHong Kong · China2023–2024 · 3 papers🎓Shanghai Jiao Tong UniversityShanghai · China2013 · 2 papers

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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksBias, fairness & safety · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Fairness-guided Few-shot Prompting for Large Language Models (2023 · 64 citations)
    In-context learning in large language models is unstable: accuracy can change sharply depending on which few-shot demonstrations are chosen and how they are ordered.
  2. A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026)
    RL-based post-training is now a leading way to strengthen reasoning in LLMs, but it is held back by data scarcity in two forms.

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. Fairness-guided Few-shot Prompting for Large Language ModelsarXiv:2303.13217Huan Ma, Changqing Zhang, Yatao Bian, et al.2023 · 64 citations
  2. Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning Incentivization ↗arXiv:2504.05812 · no summary yetQingyang Zhang et al.2025 · 31 citations
  3. Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking ↗arXiv (Cornell University) · no summary yetOctavian-Eugen Ganea et al.2021 · 29 citations
  4. A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and SolutionsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhiyin Yu, Yuchen Mou, Juncheng Yan, et al.2026

 Co-authors here

12 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 73 times (of the 149,439 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated

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