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Shangjie Li

Shangjie Li has 2 papers in our library, in 2023, most of them on Language models & pretraining, Multilingual & cross-lingual NLP; often with Deyi Xiong, Haoran Wei, Huan Lin.

2 papers in our library · 2023

OpenAlex · ORCID · h-index 5 · 7 works · 59 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 107 times in our library · ⭐ Paper Stars ranking: #7348

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

🏢ByteDanceBeijing · China2025 · 1 papercurrent🎓Tianjin UniversityTianjin · China2023–2024 · 6 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 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. PolyLM: An Open Source Polyglot Large Language Model (2023 · 103 citations)
    Most open LLMs are trained mainly on English, so they handle many widely spoken languages poorly. Open multilingual instruction data and a shared multilingual evaluation suite are also missing. Researchers at Alibaba's DAMO Academy tackle all three gaps. They pre-train PolyLM from scratch in two sizes, 1.7B and 13B…
  2. PEIT: Bridging the Modality Gap with Pre-trained Models for End-to-End Image Translation (2023 · 32 citations)
    Image translation (IT) converts text embedded in an image from one language into another. Cascaded OCR+MT pipelines accumulate errors and are slow. End-to-end models struggle because visual text and textual MT inputs are represented very differently. The authors propose PEIT, which uses pre-trained components to…

 Co-authors here

8 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 107 times (of the 149,439 papers whose bibliographies we read); 27 citing papers with a page here →.

Updated

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