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Jianhui Pang

Jianhui Pang has 3 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning, Machine translation; often with Derek F. Wong, Dian Yu, Rui Wang.

3 papers in our library · 2024–2025

OpenAlex · h-index 4 · 6 works · 64 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 255 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5766

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 MacauMacao · China2024–2025 · 6 paperscurrent🏢TencentShenzhen · China2024 · 2 papers

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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 2 worksMachine translation · 1 work

 Works by year

2020s

20252 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs (2024 · 192 citations)
    Reasoning models in the o1 family get better accuracy by writing very long chains of thought. This paper argues that much of that extra test-time compute is wasted. The authors call the waste "overthinking": after a model already has the right answer to a trivial question, it keeps producing new solution attempts.…
  2. Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models (2025 · 65 citations)
    The paper asks whether the six classic obstacles for neural MT identified by Koehn and Knowles (2017) still apply when translation is done by decoder-only LLMs.
  3. Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models (2025 · 16 citations)
    Large Reasoning Models such as OpenAI o1, DeepSeek R1 and QwQ solve hard math and coding problems by producing long chains of thought.

All works

4 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. Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMsarXiv:2412.21187Xingyu Chen, Jiahao Xu, Tian Liang, et al.2024 · 192 citations
  2. Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language ModelsTransactions of the Association for Computational Linguistics (TACL)Jianhui Pang, Fanghua Ye, Derek Fai Wong, et al.2025 · 65 citations
  3. Benchmarking and Improving Long-Text Translation with Large Language Models ↗Findings of the Association for Computational Linguistics: ACL 2024 · no summary yetLongyue Wang et al.2024 · 26 citations
  4. Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language ModelsarXiv:2503.24377Rui Wang, Hongru Wang, Boyang Xue, et al.2025 · 16 citations

 Co-authors here

12 of 22 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 255 times (of the 149,439 papers whose bibliographies we read); 91 citing papers with a page here →.

Updated

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