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Jiawei Zhou

Jiawei Zhou has 4 papers in our library, in 2025–2026, most of them on Evaluation & benchmarks, Language models & pretraining, Code & programming languages; often with Chi Zhang, Dengpan Ye, Haiyun Jiang.

4 papers in our library · 2025–2026

OpenAlex · h-index 0 · 3 works · 0 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 57 times in our library · ⭐ Paper Stars ranking: #8614

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

🎓Shanghai Jiao Tong UniversityShanghai · China2026 · 1 papercurrent🎓Stony Brook UniversityStony Brook · United States2026 · 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 6The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArts and Humanities · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 worksInformation Systems · 2 works

🧭 Topics 4Research subjects and languages the work is about.Evaluation & benchmarks · 2 worksInformation retrieval & search · 1 workCode & programming languages · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20263 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models (2025 · 63 citations)
    Process-level reward models (PRMs) score each intermediate step of an LLM's reasoning chain. Existing benchmarks mostly check whether a step is right or wrong, so they miss subtler failures. This paper introduces PRMBench, a math-reasoning benchmark that tests whether PRMs can spot different kinds of step-level…
  2. ARK: Answer-Centric Retriever Tuning via KG-augmented Curriculum Learning (2026 · 3 citations)
    ARK addresses a weakness of retrievers in long-context RAG. They are trained to match queries to similar text, so they often miss sparse passages that actually let a generator produce the right answer. The authors propose a fine-tuning recipe that targets answer usefulness instead of surface similarity.
  3. Omni-I2C: A Holistic Benchmark for High-Fidelity Image-to-Code Generation (2026)
    Omni-I2C is a benchmark that tests whether Large Multimodal Models (LMMs) can turn structured digital graphics into executable code that reproduces the original image.
  4. Position: Scores Without Context? Rethinking the Role of Evaluation in the Era of LLMs (2026)
    This position paper examines how NLP evaluation has changed since large language models became dominant. Benchmarks once tested generalization from known training data to held-out test sets. Now they mostly measure what a system can do, while training corpora stay large and often undisclosed. The author argues that a…

 Co-authors here

9 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 57 times (of the 149,439 papers whose bibliographies we read); 20 citing papers with a page here →.

Updated

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