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Jingyi Wang

Jingyi Wang has 2 papers in our library, in 2025–2026, most of them on Question answering, Multilingual & cross-lingual NLP, Evaluation & benchmarks; often with Bohan Yu, Jun Zhou, Junnan Zhu.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 3 · 6 works · 21 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 5 times in our library · ⭐ Paper Stars ranking: #10287

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

🎓Shenzhen UniversityShenzhen · China2026 · 1 papercurrent🎓Jiangsu UniversityZhenjiang · China2018–2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions 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 3Research subjects and languages the work is about.Evaluation & benchmarks · 1 workMultilingual & cross-lingual NLP · 1 workQuestion answering · 1 work

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answering (2025 · 7 citations)
    TableEval addresses weaknesses in existing table question answering (TableQA) benchmarks. Most rely on simple flat tables, cover a single language, and may overlap with LLM pretraining data. The authors gathered tables from documents published in 2024, drawn from government, finance, academic and industry-report…
  2. Training-Free Adaptive Speculative Decoding via Linguistic Priors (2026)
    Speculative decoding speeds up LLM inference: a small drafter proposes tokens and a large target model checks them in parallel.

 Co-authors here

5 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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