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Shangqian Gao

Shangqian Gao has 2 papers in our library, in 2026, most of them on Language models & pretraining, Code & programming languages; often with Dawei Xiang, Jinghan Cao, Tingting Yu.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 16 · 80 works · 841 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 2 times in our library · ⭐ Paper Stars ranking: #12306

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 PittsburghPittsburgh · United States2019–2026 · 33 paperscurrent🎓Florida State UniversityTallahassee · United States2024–2026 · 29 paperscurrent🎓Northeastern UniversityBoston · United States2016–2018 · 3 papers🏢SamsungRidgefield Park · United States2024 · 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksInformation Systems · 2 worksLanguage and Linguistics · 2 worksSoftware · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksCode & programming languages · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. TreeDiff: AST-Guided Code Generation with Diffusion LLMs (2026 · 3 citations)
    Diffusion-based language models (DLLMs) generate text by iteratively un-masking a corrupted sequence. They do this poorly for code, partly because training masks tokens uniformly at random, ignoring program grammar. As a result, models often produce syntactically broken drafts and miss long-range relations such as…
  2. HyperEdit: Unlocking Instruction-based Text Editing in LLMs via Hypernetworks (2026)
    LLM-based text editing often fails in two ways. Models may not carry out the user's instruction precisely, and they tend to rewrite parts of the text that should stay untouched, which can break code or degrade a corrected document. HyperEdit addresses both problems. A hypernetwork reads the instruction and the source…

 Co-authors here

9 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 2 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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