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Yujian Gan

Yujian Gan has 3 papers in our library, in 2021–2024, most of them on Code & programming languages, Semantic parsing & meaning representation, Coreference resolution; often with Matthew Purver, Xinyun Chen, Juntao Yu.

3 papers in our library · 2021–2024

OpenAlex · ORCID · h-index 7 · 25 works · 288 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 126 citations in our library · ⭐ Paper Star rank #4238

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

🎓Guangxi University of Finance and EconomicsNanning · China2014–2026 · 4 paperscurrent🎓Queen Mary University of LondonLondon · United Kingdom2020–2025 · 15 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.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5 Arts and Humanities · 3 works Computer Science · 3 works Artificial Intelligence · 3 works Language and Linguistics · 3 works Computer Vision and Pattern Recognition · 1 work

🧭 Topics 3 Code & programming languages · 2 works Semantic parsing & meaning representation · 2 works Coreference resolution · 1 work

 Works by year

2020s

20241 paper20212 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Towards Robustness of Text-to-SQL Models against Synonym Substitution (2021 · 143 citations)
    Cross-domain text-to-SQL systems score well on benchmarks such as Spider. Much of that success, however, comes from schema linking, which matches question words literally against table and column names. Gan et al. examine what happens when users refer to schema items or cell values with different but equivalent…
  2. Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization (2021 · 123 citations)
    Cross-domain text-to-SQL systems score highly on Spider, but it is unclear whether they truly learn the domain knowledge implicit in their training data.
  3. Assessing the Capabilities of Large Language Models in Coreference: An Evaluation (2024 · 24 citations)
    The authors ask how well modern LLMs handle coreference and what this means for coreference research. They tested the GPT series (GPT3.5, GPT4, version 0613, temperature 0), the LLAMA2 family and Bard on two settings. The first was whole-document anaphora resolution on documents from the CRAC 2018 and CODI-CRAC…

 Co-authors here

4 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 126 times (of the 141,840 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

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