Yichang Zhang
Yichang Zhang has 5 papers in our library, in 2024–2026, most of them on Language models & pretraining, Reasoning, Evaluation & benchmarks; often with Dayiheng Liu, Junyang Lin, An Yang.
OpenAlex · ORCID · h-index 17 · 54 works · 1,324 citations OpenAlex, 2026-10-14
📄 5 works in our library · 🔗 Cited 5,099 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7353
Affiliations
Fields, topics and concepts
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 5 worksComputer Science · 5 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 5 worksComputer Vision and Pattern Recognition · 2 works
🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 2 worksEvaluation & benchmarks · 1 work
Works by year
2020s
Papers
- Qwen3Guard Technical Report (2025 · 33 citations)
Guardrail classifiers that screen LLM traffic usually produce only a binary safe/unsafe verdict, which different safety policies interpret differently. - Language Models can Self-Lengthen to Generate Long Texts (2024 · 13 citations)
Open models such as Qwen2 and LLaMA3 read long inputs well but rarely produce coherent answers longer than about 2,000 words. - Confidence v.s. Critique: A Decomposition of Self-Correction Capability for LLMs (2025 · 11 citations)
Intrinsic self-correction means asking an LLM to re-check and revise its own answer without external feedback. - MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation (2026 · 9 citations)
Most reasoning benchmarks for LLMs test single-turn problems. Real problem solving is often interactive: a solver asks questions, gets feedback and revises its plan. Earlier multi-turn efforts such as MT-Bench and GameArena either focus on dialogue coherence or depend on costly human involvement. - Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models (2026 · 3 citations)
Generative reward models (GenRMs) and LLM-as-a-Judge systems are usually trained and evaluated only on whether they pick the preferred response.
All works
- Towards Knowledge-Based Recommender Dialog System ↗Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · no summary yetQibin Chen et al.2019 · 282 citations
- Qwen3Guard Technical ReportarXiv:2510.14276Haiquan Zhao, Chenhan Yuan, Fei Huang, et al.2025 · 33 citations
- Language Models can Self-Lengthen to Generate Long TextsarXiv:2410.23933Shanghaoran Quan, Tianyi Tang, Bowen Yu, et al.2024 · 13 citations
- Confidence v.s. Critique: A Decomposition of Self-Correction Capability for LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhe Yang, Yichang Zhang, Yudong Wang, et al.2025 · 11 citations
- MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning EvaluationAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaoyuan Li, Keqin Bao, Yubo Ma, et al.2026 · 9 citations
- Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Binghai Wang, Yantao Liu, Yuxuan Liu, et al.2026 · 3 citations
Co-authors here
- Dayiheng Liu ×3
- Junyang Lin ×3
- An Yang ×2
- Bowen Yu ×2
- Jingren Zhou ×2
- Tianyi Tang ×2
- Baosong Yang
- Binghai Wang
- Bofei Gao
- Chang Gao
- Chujie Zheng
- Fei Huang
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×3: ACL 2026 ×2, ACL 2025
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Cited by
Updated
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