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Yancheng He

Yancheng He has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining, Machine translation, Dialogue systems; often with Jiaheng Liu, Bo Zheng, Chenchen Zhang.

2 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 12 · 55 works · 535 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 240 times in our library · 5 more in our library without a page yet · ⭐ Paper Stars ranking: #4501

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 · 7 paperscurrent🎓Harbin Institute of TechnologyHarbin · China2010–2025 · 5 paperscurrent🎓Guangxi Normal UniversityGuilin · China2011–2025 · 2 paperscurrent🏢TencentShenzhen · China2019–2024 · 19 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.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 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

20251 paperA Comprehensive Survey on Long Context Language Modeling20241 paperMT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues

 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 · 171 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. A Comprehensive Survey on Long Context Language Modeling (2025 · 64 citations)
    Language models that can read very long inputs, such as whole books, extended dialogues or large codebases, are now common.

All works

7 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn DialoguesGe Bai, Jie Liu, Xingyuan Bu, et al.Annual Meeting of the Association for Computational Linguistics (ACL) · 171 citations
  2. A Comprehensive Survey on Long Context Language ModelingJiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.arXiv:2503.17407 · 64 citations
  3. Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models · original ↗ · no summary yet · arXiv:2411.07140Yancheng He et al.2024 · 34 citations
  4. Align Voting Behavior with Public Statements for Legislator Representation Learning · original ↗ · no summary yet · Proceedings of the 59th Annual Meeting of the Association for Computational…Xinyi Mou et al.2021 · 25 citations
  5. Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning · original ↗ · no summary yet · arXiv:2508.08221Zihe Liu et al.2025 · 15 citations
  6. MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training · original ↗ · no summary yet · Proceedings of the 63rd Annual Meeting of the Association for Computational…Hui Huang et al.2025 · 4 citations
  7. Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models · original ↗ · no summary yet · arXiv:2412.15265Yingshui Tan et al.2024 · 4 citations

 Co-authors here

11 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 240 times (of the 141,840 papers whose bibliographies we read); 10 citing papers with a page here →.

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