---
title: "Wenyue Hua — author profile and papers — Elamur Research"
description: "Wenyue Hua, Microsoft: 6 papers on Elamur Research (2024–2025), h-index 14 — affiliations, topics, co-authors and who cites them in our library."
canonical: "https://research.elamur.ai/free/en/authors/wenyue-hua/"
---
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# Wenyue Hua

Wenyue Hua has 6 papers in our library, in 2024–2025, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/), [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/); often with [Mingyu Jin](https://md.research.elamur.ai/free/en/authors/mingyu-jin/), [William Yang Wang](https://md.research.elamur.ai/free/en/authors/william-yang-wang/), [Yongfeng Zhang](https://md.research.elamur.ai/free/en/authors/yongfeng-zhang/).

6 papers in our library · 2024–2025

[OpenAlex](https://openalex.org/A5042013742) · [ORCID](https://orcid.org/0009-0008-2043-2704) · [Wikidata](https://www.wikidata.org/wiki/Q130820344) · [**h-index 14**](https://openalex.org/authors/A5042013742) · [79 works](https://openalex.org/works?filter=author.id:A5042013742) · [640 citations](https://openalex.org/works?filter=author.id:A5042013742&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 6 works in our library](https://md.research.elamur.ai/free/en/authors/wenyue-hua/#papers) · [🔗 Cited 250 times in our library](https://md.research.elamur.ai/free/en/authors/wenyue-hua/cited-by/) · [2 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #5145](https://md.research.elamur.ai/free/en/paper-stars/page/52/#star-5145)

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Wenyue+Hua) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Wenyue+Hua) · [dblp](https://dblp.org/search?q=Wenyue+Hua) · [ACL Anthology](https://aclanthology.org/search/?q=Wenyue+Hua)

## Affiliations

[🎓**University of California, Santa Barbara** Santa Barbara · United States 2024–2026 · 13 papers*current*](https://md.research.elamur.ai/free/en/universities/university-of-california-santa-barbara/) · [🏢**Microsoft** Redmond · United States 2026 · 10 papers*current*](https://md.research.elamur.ai/free/en/companies/microsoft/) · [🏢**Microsoft Research** Cambridge · United Kingdom 2026 · 2 papers*current*](https://md.research.elamur.ai/free/en/companies/microsoft-research-united-kingdom/) · [🏢**Microsoft Research New York City** New York 2026 · 1 paper*current*](https://ror.org/056zprp28) · [🎓**Rutgers, The State University of New Jersey** New Brunswick · United States 2021–2025 · 34 papers](https://md.research.elamur.ai/free/en/universities/rutgers-the-state-university-of-new-jersey/) · [🎓**University of Michigan** Ann Arbor · United States 2024–2025 · 2 papers](https://md.research.elamur.ai/free/en/universities/university-of-michigan/)

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 5 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/)

🧭 Topics 3 Research subjects and languages the work is about.[Language models & pretraining · 5 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning · 4 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Evaluation & benchmarks · 2 works](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/)

## Works by year

### 2020s

[**2025** 4 papers](https://md.research.elamur.ai/free/en/authors/wenyue-hua/year/2025/) · [**2024** 2 papers](https://md.research.elamur.ai/free/en/authors/wenyue-hua/year/2024/)

## Papers

The most cited first (OpenAlex and our library).

1. **[The Impact of Reasoning Step Length on Large Language Models](https://md.research.elamur.ai/free/en/papers/jin-2024-impact-reasoning-step-length-large/)** *(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](https://md.research.elamur.ai/free/en/papers/fan-2024-nphardeval-dynamic-benchmark-reasoning-ability/)** *(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](https://md.research.elamur.ai/free/en/papers/jin-2025-disentangling-memory-reasoning-ability-large/)** *(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?](https://md.research.elamur.ai/free/en/papers/jin-2025-exploring-concept-depth-how-large/)** *(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](https://md.research.elamur.ai/free/en/papers/zhou-2025-rulearena-benchmark-rule-guided-reasoning/)** *(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](https://md.research.elamur.ai/free/en/papers/hua-2025-inductionbench-llms-fail-simplest-complexity/)** *(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 Models](https://md.research.elamur.ai/free/en/papers/jin-2024-impact-reasoning-step-length-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mingyu 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 ↗](https://arxiv.org/abs/2311.17227) arXiv:2311.17227 · no summary yet**Wenyue Hua et al.*2023 · 54 citations*
3. **[NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes](https://md.research.elamur.ai/free/en/papers/fan-2024-nphardeval-dynamic-benchmark-reasoning-ability/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lizhou Fan, Wenyue Hua, Lingyao Li, et al.*2024 · 47 citations*
4. **[Disentangling Memory and Reasoning Ability in Large Language Models](https://md.research.elamur.ai/free/en/papers/jin-2025-disentangling-memory-reasoning-ability-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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?](https://md.research.elamur.ai/free/en/papers/jin-2025-exploring-concept-depth-how-large/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/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 Scenarios](https://md.research.elamur.ai/free/en/papers/zhou-2025-rulearena-benchmark-rule-guided-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruiwen Zhou, Wenyue Hua, Liangming Pan, et al.*2025 · 21 citations*
7. **[InductionBench: LLMs Fail in the Simplest Complexity Class](https://md.research.elamur.ai/free/en/papers/hua-2025-inductionbench-llms-fail-simplest-complexity/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenyue Hua, Tyler Wong, Fei Sun, et al.*2025 · 5 citations*
8. **[Mixed-domain Language Modeling for Processing Long Legal Documents ↗](https://aclanthology.org/2023.nllp-1.7/) Proceedings of the Natural Legal Language Processing Workshop 2023 · no summary yet**Wenyue Hua et al.*2023 · 4 citations*

## Co-authors here

12 of 23 co-authors with a page, the most papers together first.

- [Mingyu Jin](https://md.research.elamur.ai/free/en/authors/mingyu-jin/) ×3
- [William Yang Wang](https://md.research.elamur.ai/free/en/authors/william-yang-wang/) ×3
- [Yongfeng Zhang](https://md.research.elamur.ai/free/en/authors/yongfeng-zhang/) ×3
- [Haiyan Zhao](https://md.research.elamur.ai/free/en/authors/haiyan-zhao/) ×2
- [Liangming Pan](https://md.research.elamur.ai/free/en/authors/liangming-pan/) ×2
- [Mengnan Du](https://md.research.elamur.ai/free/en/authors/mengnan-du/) ×2
- [Qinkai Yu](https://md.research.elamur.ai/free/en/authors/qinkai-yu/) ×2
- [Sitao Cheng](https://md.research.elamur.ai/free/en/authors/sitao-cheng/) ×2
- [Yanda Meng](https://md.research.elamur.ai/free/en/authors/yanda-meng/) ×2
- [Adam Jardine](https://md.research.elamur.ai/free/en/authors/adam-jardine/)
- [Dong Shu](https://md.research.elamur.ai/free/en/authors/dong-shu/)
- [Fan Yang](https://md.research.elamur.ai/free/en/authors/fan-yang/)

## 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)](https://md.research.elamur.ai/free/en/venues/acl/) ×4: [ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/) ×3, [ACL 2024](https://md.research.elamur.ai/free/en/venues/acl/2024/)
- [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/): [COLING 2025](https://md.research.elamur.ai/free/en/venues/coling/2025/)
- [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2024](https://md.research.elamur.ai/free/en/venues/findings/2024/)
- Proceedings of the Natural Legal Language Processing Workshop 2023

## 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 →](https://md.research.elamur.ai/free/en/authors/wenyue-hua/cited-by/).

Updated 31 October 2026