🧑‍🔬 Xingyuan Bu

🏛️ Affiliations

🎓Beijing Institute of TechnologyBeijing · China2008–2026 · 16 paperscurrent🎓Nanjing University of Aeronautics and AstronauticsNanjing · China2026 · 2 paperscurrent🏢ByteDanceBeijing · China2026 · 1 papercurrent🏢Alibaba GroupHangzhou · China2022–2025 · 14 papers🏢SensetimeBeijing · China2020–2021 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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.

📅 Works by year

2020s

📄 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…
3. Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning? (2025 · 30 citations)
   Reasoning models in the o1 style produce very long chains of thought (CoT). Until this paper, there was no systematic way to check how good those chains are, or whether other models can find the mistakes in them. The authors built DeltaBench to address both gaps. It contains 1,236 problems from Math, Programming, PCB…

All works

5 works of theirs in our library, the most cited first; 3 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 DialoguesAnnual Meeting of the Association for Computational Linguistics (ACL)Ge Bai, Jie Liu, Xingyuan Bu, et al.2024 · 174 citations
2. SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines ↗arXiv:2502.14739 · no summary yetM-A-P Team et al.2025 · 89 citations
3. Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language ModelsarXiv:2411.07140Yancheng He, Shilong Li, Jiaheng Liu, et al.2024 · 34 citations
4. Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning?Annual Meeting of the Association for Computational Linguistics (ACL)Yancheng He, Shilong Li, Jiaheng Liu, et al.2025 · 30 citations
5. COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values ↗Findings of the Association for Computational Linguistics: EACL 2026 · no summary yetSiwei Wu et al.2026

🤝 Co-authors here

12 of 19 co-authors with a page, the most papers together first.

• Jiaheng Liu ×3
• Yancheng He ×3
• Bo Zheng ×2
• Shilong Li ×2
• Weixun Wang ×2
• Wenbo Su ×2
• Zhuoran Lin ×2
• Boren Zheng
• Ge Zhang
• Hangyu Guo
• Hui Huang
• Jie Liu

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2025, ACL 2024
• Findings of the Association for Computational Linguistics: EACL 2026

🔗 Cited by

Papers of our library cite their works 258 times (of the 149,439 papers whose bibliographies we read); 83 citing papers with a page here →.

Updated 1 November 2026

↑ back to top

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