Jian Zhang
Jian Zhang has 2 papers in our library, in 2016–2023, most of them on Language models & pretraining; often with Bo Li, Percy Liang, Pranav Rajpurkar.
OpenAlex · ORCID · h-index 33 · 360 works · 3,878 citations OpenAlex, 2026-10-14
📄 2 works in our library · 🔗 Cited 3,401 times in our library · ⭐ Paper Stars ranking: #273
Affiliations
🎓Shanghai International Studies UniversityShanghai ·
China2003–2026 · 35 papers🎓China University of Mining and TechnologyXuzhou ·
China2019–2026 · 6 papers🎓Liaoning UniversityShenyang ·
China2022–2026 · 4 papers🎓Qinhuangdao Vocational and Technical CollegeQinhuangdao ·
China2020–2026 · 2 papers🎓University of Hong KongPok Fu Lam ·
Hong Kong2017–2025 · 11 papers🎓East China University of Science and TechnologyShanghai ·
China2013–2025 · 6 papers🎓Shandong UniversityJinan ·
China2010–2023 · 6 papers🎓University of Science and Technology BeijingBeijing ·
China2014–2022 · 6 papers🎓Southwestern University of Finance and EconomicsChengdu ·
China2016–2019 · 6 papers🔬World BankWashington ·
United States2010–2014 · 6 papers
Concepts, shelves and topics
📚 Shelves 5The 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 works
🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work
Works by year
2020s
2010s
Papers
- SQuAD: 100,000+ Questions for Machine Comprehension of Text (2016 · 9,513 citations)
Machine reading comprehension lacked a benchmark that was both large and natural. Carefully curated sets such as MCTest are too small for data-hungry models. Very large cloze-style corpora such as CNN/Daily Mail and CBT are semi-synthetic. To fill this gap, the authors built SQuAD. Crowdworkers read paragraphs from… - On the Tool Manipulation Capability of Open-source Large Language Models (2023 · 88 citations)
Most work on LLM-driven tool use relies on proprietary APIs. That is a problem for companies that cannot send internal workflows and data to external services. The authors ask whether open-source LLMs can match closed models at turning natural-language goals into executable API calls without heavy human effort.
Co-authors here
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