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Li Li

Li Li has 2 papers in our library, in 2026, most of them on Language models & pretraining; often with Houqiang Li, Jianfeng Cai, Jinhua Zhu.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 14 · 135 works · 962 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 4 times in our library · ⭐ Paper Stars ranking: #10721

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

🏛️Chinese Academy of SciencesBeijing · China1999–2026 · 29 paperscurrent🔬Institute of High Energy PhysicsBeijing · China2009–2026 · 24 paperscurrent🎓Hubei University of Science and TechnologyXianning · China2005–2026 · 2 paperscurrent🎓Nanjing Institute of TechnologyNanjing · China2026 · 2 paperscurrent🎓University of Science and Technology of ChinaHefei · China2026 · 1 papercurrent🏢State Grid Corporation of ChinaBeijing · China2015–2020 · 6 papers🎓Ghent UniversityGhent · Belgium2011–2016 · 4 papers🏛️Key Laboratory of Nuclear Radiation and Nuclear Energy TechnologyShanghai · China2014–2015 · 4 papers🔬China Institute of Atomic EnergyBeijing · China1998–2011 · 3 papers🎓Guangdong University of TechnologyGuangzhou · China2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 40 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 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge (2026 · 2 citations)
    Federated fine-tuning lets LLMs be adapted to private data that never leaves user devices. However, even adapter-based approaches still require the whole model in memory, which excludes phones and other edge hardware with roughly 4–12GB. The authors show that frozen base weights, not activations or adapters, dominate…
  2. Bias Fitting to Mitigate Length Bias of Reward Model in RLHF (2026 · 2 citations)
    Reward models used in RLHF often give higher scores to longer answers whether or not those answers are better.

 Co-authors here

5 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 4 times (of the 149,439 papers whose bibliographies we read).

Updated

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