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Zhuoran Lin

Zhuoran Lin has 2 papers in our library, in 2024, most of them on Language models & pretraining, Machine translation, Dialogue systems; often with Jiaheng Liu, Xingyuan Bu, Yancheng He.

2 papers in our library · 2024

OpenAlex · h-index 5 · 13 works · 132 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 147 times in our library · ⭐ Paper Stars ranking: #6380

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

🏢Alibaba GroupHangzhou · China2024–2025 · 3 paperscurrent🎓Tianjin UniversityTianjin · China2021–2024 · 5 papers

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).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 2 worksDialogue systems · 1 workEvaluation & benchmarks · 1 workMachine translation · 1 work

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues (2024 · 174 citations)
    Existing LLM benchmarks mostly test single-turn instructions. Multi-turn ones such as MT-Bench cover only two turns and broad skill categories, so they miss much of what happens in real chatbot conversations. MT-Bench-101 fills this gap with a fine-grained multi-turn benchmark. The authors studied real user–chatbot…
  2. Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models (2024 · 34 citations)
    Hallucination remains a major weakness of large language models. Factuality is hard to measure when models produce long answers that mix many claims. OpenAI's SimpleQA addressed this with short fact-seeking questions, but it covers only English. Chinese SimpleQA is a Chinese counterpart: 3,000 short questions, each…

 Co-authors here

12 of 16 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 147 times (of the 149,439 papers whose bibliographies we read); 47 citing papers with a page here →.

Updated

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