Elamur Research⌕
☰
home / authors / Yi Zhao

Yi Zhao

Yi Zhao has 3 papers in our library, in 2026, most of them on Language models & pretraining, Reasoning; often with Cam-Tu Nguyen, Hai Zhao, Jie Tang.

3 papers in our library · 2026

OpenAlex · h-index 1 · 4 works · 3 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 1 time in our library · ⭐ Paper Stars ranking: #11957

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

🎓Anhui UniversityHefei · China2026 · 1 papercurrent🎓Renmin University of ChinaBeijing · China2026 · 1 papercurrent🎓Shanghai Jiao Tong UniversityShanghai · China2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArts and Humanities · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksHistory and Philosophy of Science · 1 workComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment (2026 · 4 citations)
    Novelty is a central criterion in peer review, and rising submission volumes are straining reviewers. LLMs can already draft review comments, but no benchmark existed to test whether they can judge novelty specifically. Most prior evaluation either scored whole reviews or relied on lexical metrics such as ROUGE/BLEU…
  2. PlotGen-Bench: Evaluating VLMs on Generating Visualization Code from Diverse Plots across Multiple Libraries (2026)
    Vision-language models can already read and reason about charts, but their ability to write runnable code that recreates a given plot has been tested only narrowly.
  3. TrigReason: Trigger-Based Collaboration between Small and Large Reasoning Models (2026)
    Large Reasoning Models (LRMs) solve hard problems well, but their long chains of thought make inference slow.

 Co-authors here

10 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 1 time (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/yi-zhao/