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Yi Zong

Yi Zong has 2 papers in our library, in 2023–2024, most of them on Evaluation & benchmarks, Language models & pretraining, Vision & language; often with Xipeng Qiu, Chunyang Li, Jun Zhan.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 3 · 4 works · 30 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 96 times in our library · ⭐ Paper Stars ranking: #6553

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

🎓Fudan UniversityShanghai · China2023–2025 · 3 paperscurrent

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).Computer Science · 2 worksArts and Humanities · 1 workArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 2 worksLanguage models & pretraining · 1 workVision & language · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Evaluating the Performance of Large Language Models on GAOKAO Benchmark (2023 · 109 citations)
    Popular LLM benchmarks such as MMLU, C-Eval and AGIEval consist almost entirely of objective items. They reveal little about generative ability and give no intuitive, human-comparable sense of model skill. The authors address this gap with GAOKAO-Bench, a dataset of national Chinese college entrance exam (GAOKAO)…
  2. GAOKAO-MM: A Chinese Human-Level Benchmark for Multimodal Models Evaluation (2024 · 11 citations)
    Most vision-language benchmarks test basic perception or commonsense with short prompts. Chinese versions are usually translations of English datasets. As a result, they reveal little about how large vision-language models (LVLMs) cope with demanding, natively Chinese material. Zong and Qiu fill this gap with…

 Co-authors here

8 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 96 times (of the 149,439 papers whose bibliographies we read); 35 citing papers with a page here →.

Updated

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