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Mingyu Jin

Mingyu Jin has 3 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning; often with Wenyue Hua, Haiyan Zhao, Mengnan Du.

3 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 15 · 53 works · 943 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 154 times in our library · ⭐ Paper Stars ranking: #5688

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

🎓Rutgers, The State University of New JerseyNew Brunswick · United States2024–2025 · 21 papersEmployment · Computer Sciencecurrent🎓Dongguan University of TechnologyDongguan · China2023–2025 · 20 paperscurrent🎓South China Agricultural UniversityGuangzhou · China2020–2024 · 4 papers🎓Northwestern UniversityEvanston · United States2023–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3 worksReasoning · 2 works

 Works by year

2020s

20252 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. The Impact of Reasoning Step Length on Large Language Models (2024 · 119 citations)
    Chain of Thought (CoT) prompting helps large language models reason, but it is unclear which part of a CoT prompt drives the gains.
  2. Disentangling Memory and Reasoning Ability in Large Language Models (2025 · 45 citations)
    Large language models typically answer knowledge-heavy questions with one undivided chain of thought. This makes it hard to tell whether a wrong answer comes from missing facts or from faulty logic, and the blending is associated with knowledge being lost mid-chain and with hallucination.
  3. Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? (2025 · 36 citations)
    The paper asks where inside a large language model knowledge of different difficulty becomes linearly readable.

 Co-authors here

12 of 18 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 154 times (of the 149,439 papers whose bibliographies we read); 36 citing papers with a page here →.

Updated

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