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Wenyue Hua

Wenyue Hua has 6 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning, Evaluation & benchmarks; often with Mingyu Jin, William Yang Wang, Yongfeng Zhang.

6 papers in our library · 2024–2025

OpenAlex · ORCID · Wikidata · h-index 14 · 79 works · 640 citations OpenAlex, 2026-10-14

📄 6 works in our library · 🔗 Cited 250 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5145

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

🎓University of California, Santa BarbaraSanta Barbara · United States2024–2026 · 13 paperscurrent🏢MicrosoftRedmond · United States2026 · 10 paperscurrent🏢Microsoft ResearchCambridge · United Kingdom2026 · 2 paperscurrent🏢Microsoft Research New York CityNew York2026 · 1 papercurrent🎓Rutgers, The State University of New JerseyNew Brunswick · United States2021–2025 · 34 papers🎓University of MichiganAnn Arbor · United States2024–2025 · 2 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 6 worksComputer Science · 6 worksArtificial Intelligence · 6 worksLanguage and Linguistics · 6 worksComputer Vision and Pattern Recognition · 3 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 5 worksReasoning · 4 worksEvaluation & benchmarks · 2 works

 Works by year

2020s

20254 papers20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. The Impact of Reasoning Step Length on Large Language Models (2024 · 119 citations)
    Chain of Thought (CoT) prompting helps large language models reason, but it is unclear which part of a CoT prompt drives the gains.
  2. NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes (2024 · 47 citations)
    Most reasoning benchmarks for LLMs are fixed and publicly available, so models can memorize them or be tuned toward them, which inflates scores.
  3. Disentangling Memory and Reasoning Ability in Large Language Models (2025 · 45 citations)
    Large language models typically answer knowledge-heavy questions with one undivided chain of thought. This makes it hard to tell whether a wrong answer comes from missing facts or from faulty logic, and the blending is associated with knowledge being lost mid-chain and with hallucination.
  4. Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? (2025 · 36 citations)
    The paper asks where inside a large language model knowledge of different difficulty becomes linearly readable.
  5. RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World Scenarios (2025 · 21 citations)
    Benchmarks of LLM instruction-following mostly check style constraints such as format or length, or they test simple first-order logic. RuleArena instead tests whether models can reason under real regulations written in natural language. The authors gathered 95 rules from three domains:
  6. InductionBench: LLMs Fail in the Simplest Complexity Class (2025 · 5 citations)
    Most reasoning benchmarks for LLMs test deduction: the rules are given (axioms, programming syntax) and must be applied.

All works

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

  1. The Impact of Reasoning Step Length on Large Language ModelsFindings of the Association for Computational LinguisticsMingyu Jin, Qinkai Yu, Dong Shu, et al.2024 · 119 citations
  2. War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars ↗arXiv:2311.17227 · no summary yetWenyue Hua et al.2023 · 54 citations
  3. NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity ClassesAnnual Meeting of the Association for Computational Linguistics (ACL)Lizhou Fan, Wenyue Hua, Lingyao Li, et al.2024 · 47 citations
  4. Disentangling Memory and Reasoning Ability in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Mingyu Jin, Weidi Luo, Sitao Cheng, et al.2025 · 45 citations
  5. Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?International Conference on Computational Linguistics (COLING)Mingyu Jin, Qinkai Yu, Jingyuan Huang, et al.2025 · 36 citations
  6. RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World ScenariosAnnual Meeting of the Association for Computational Linguistics (ACL)Ruiwen Zhou, Wenyue Hua, Liangming Pan, et al.2025 · 21 citations
  7. InductionBench: LLMs Fail in the Simplest Complexity ClassAnnual Meeting of the Association for Computational Linguistics (ACL)Wenyue Hua, Tyler Wong, Fei Sun, et al.2025 · 5 citations
  8. Mixed-domain Language Modeling for Processing Long Legal Documents ↗Proceedings of the Natural Legal Language Processing Workshop 2023 · no summary yetWenyue Hua et al.2023 · 4 citations

 Co-authors here

12 of 23 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 250 times (of the 149,439 papers whose bibliographies we read); 54 citing papers with a page here →.

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