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Yiming Li

Yiming Li has 2 papers in our library, in 2025–2026, most of them on Language models & pretraining; often with Baolei Zhang, Biao Yi, Dacheng Tao.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 8 · 21 works · 239 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 17 times in our library · ⭐ Paper Stars ranking: #10252

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

🎓Shonan Institute of TechnologyFujisawa · Japan2022–2025 · 3 paperscurrent🎓Nanyang Technological UniversitySingapore · Singapore2024–2025 · 2 paperscurrent🎓China University of Petroleum, BeijingBeijing · China2019–2024 · 10 papers🎓Jilin UniversityChangchun · China2022–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 work

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

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Understanding the Dark Side of LLMs' Intrinsic Self-Correction (2025 · 21 citations)
    The paper investigates why intrinsic self-correction often makes large language models worse. In this setting, a model is asked to reconsider its answer with no external feedback or oracle labels. The authors apply feedback and refinement prompts to every response, including correct ones. They test ChatGPT (o1, 4o…
  2. SEAD: A Surrogate-free Label-only Membership Inference Attack against Pre-trained LLMs with Semantic-Aware Density (2026)
    Membership inference attacks (MIAs) test whether a given text was part of a model's training data, which matters for privacy audits, copyright checks and verifying machine unlearning.

 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 17 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated

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