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Botao Yu

Botao Yu has 3 papers in our library, in 2023–2024, most of them on Evaluation & benchmarks, Vision & language, Reasoning; often with Ge Zhang, Kai Zhang, Tianyu Zheng.

3 papers in our library · 2023–2024

OpenAlex · h-index 4 · 11 works · 432 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 788 times in our library · ⭐ Paper Stars ranking: #2363

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

🎓The Ohio State UniversityColumbus · United States2024–2026 · 4 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 8The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 2 worksArts and Humanities · 1 workDecision Sciences · 1 workLanguage and Linguistics · 1 workInformation Systems · 1 workInformation Systems and Management · 1 work

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 2 worksVision & language · 2 worksReasoning · 1 work

 Works by year

2020s

20242 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI (2023 · 1,005 citations)
    Multimodal models score very well on everyday visual benchmarks such as VQA-v2 and RefCOCO, but these tests rarely require specialist knowledge.
  2. MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark (2024 · 123 citations)
    MMMU is a widely used college-level benchmark of image–text questions, but some of its items can be solved without the image. Text-only LLMs succeed either because the picture is irrelevant or because they exploit cues in the answer choices. MMMU-Pro rebuilds the benchmark in three stages:
  3. ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery (2024 · 81 citations)
    Claims that LLM-based agents can automate whole research pipelines are hard to verify. The authors argue that agents should first be tested on the individual steps of a scientific workflow. For this purpose they introduce ScienceAgentBench, which collects 102 tasks from 44 peer-reviewed papers spanning four fields…

 Co-authors here

12 of 20 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 788 times (of the 149,439 papers whose bibliographies we read); 215 citing papers with a page here →.

Updated

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