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Lujun Li

Lujun Li has 2 papers in our library, in 2026, most of them on Machine translation, Language models & pretraining, Low-resource languages; often with Cedric Lothritz, Chao Li, Hao Gu.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 16 · 71 works · 958 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 5 times in our library · ⭐ Paper Stars ranking: #10409

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

🎓Hong Kong University of Science and TechnologyHong Kong · China2023–2026 · 29 papers🏛️Chinese Academy of SciencesBeijing · China2021–2023 · 12 papers🔬Institute of AutomationBeijing · China2021–2022 · 2 papers🎓Monash University MalaysiaSubang Jaya · Malaysia2022 · 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 4Research subjects and languages the work is about.Efficient models & compression · 1 workLanguage models & pretraining · 1 workLow-resource languages · 1 workMachine translation · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Are Small Language Models the Silver Bullet to Low-Resource Languages Machine Translation? (2026 · 3 citations)
    The paper examines whether compact language models can translate low-resource languages (LRLs) reliably. Such models are attractive where cost, latency, privacy or offline use matter, but they often hallucinate on underrepresented languages. The authors first benchmark several small decoder-only models (Llama-3.x…
  2. Bit-by-Bit: Progressive QAT Strategy with Outlier Channel Splitting for Stable Low-Bit LLMs (2026 · 3 citations)
    Quantization-aware training (QAT) of LLMs at extreme precisions, such as 2-bit weights and activations, often breaks down.

 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 5 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

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