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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.

5 papers in our library · 2024–2026

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

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

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 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. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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

6 works of theirs in our library, the most cited first; 5 with a page here, the others link their original until we summarise them.

  1. 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
  2. Qwen3Guard Technical ReportarXiv:2510.14276Haiquan Zhao, Chenhan Yuan, Fei Huang, et al.2025 · 33 citations
  3. Language Models can Self-Lengthen to Generate Long TextsarXiv:2410.23933Shanghaoran Quan, Tianyi Tang, Bowen Yu, et al.2024 · 13 citations

 Co-authors here

12 of 30 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 5,099 times (of the 149,439 papers whose bibliographies we read); 1,195 citing papers with a page here →.

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