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Caishuang Huang

Caishuang Huang has 3 papers in our library, in 2025–2026, most of them on Language models & pretraining, Named entity recognition; often with Junjie Ye, Tao Gui, Qi Zhang.

3 papers in our library · 2025–2026

OpenAlex · h-index 7 · 22 works · 108 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 127 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #7791

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

🎓Fudan UniversityShanghai · China2024–2026 · 14 paperscurrent🏛️Chinese Academy of ForestryBeijing · China2021–2023 · 3 papers🔬Institute of Forest Ecology, Environment and ProtectionBeijing · China2021–2023 · 3 papers

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

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 2 worksEngineering · 1 workArts and Humanities · 2 worksLanguage and Linguistics · 2 worksControl and Systems Engineering · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksNamed entity recognition · 1 work

 Works by year

2020s

20261 paper20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios (2025 · 35 citations)
    Most benchmarks for tool use by large language models only check whether a model picks the expected API. That approach works only when correct answers can be fixed in advance, and it reveals little about the skills behind successful tool use. ToolEyes addresses both gaps. The authors define seven realistic scenarios…
  2. Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition (2025 · 11 citations)
    Open NER requires a model to find entities of any requested type in text from any domain, and even strong LLMs such as GPT-4 perform poorly at it.
  3. MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models (2026 · 4 citations)
    Most instruction-following benchmarks for LLMs vary only the type of constraint. They say little about how constraints are phrased or how hard their combinations are, and they offer little insight into how to improve models. MulDimIF addresses this with a framework that describes each instruction along three axes…

All works

6 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. Secrets of RLHF in Large Language Models Part II: Reward Modeling ↗arXiv:2401.06080 · no summary yetBinghai Wang et al.2024 · 72 citations
  2. ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world ScenariosInternational Conference on Computational Linguistics (COLING)Junjie Ye, Guanyu Li, SongYang Gao, et al.2025 · 35 citations
  3. StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback ↗Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetShihan Dou et al.2024 · 32 citations
  4. Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity RecognitionInternational Conference on Computational Linguistics (COLING)Yuming Yang, Wantong Zhao, Caishuang Huang, et al.2025 · 11 citations
  5. MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language ModelsFindings of the Association for Computational LinguisticsJunjie Ye, Caishuang Huang, Zhuohan Chen, et al.2026 · 4 citations
  6. VRPO: Rethinking Value Modeling for Robust RL under Noisy Supervision in LLM Post-Training ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetDingwei Zhu et al.2026

 Co-authors here

12 of 14 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 127 times (of the 149,439 papers whose bibliographies we read); 22 citing papers with a page here →.

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