Elamur Research⌕
☰
home / authors / Yu-Neng Chuang

Yu-Neng Chuang

Yu-Neng Chuang has 3 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning; often with Guanchu Wang, Shaochen Zhong, Xia Hu.

3 papers in our library · 2025–2026

OpenAlex · ORCID · Wikidata · h-index 7 · 45 works · 291 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 168 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6784

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

🎓Rice UniversityHouston · United States2022–2026 · 34 paperscurrent🎓National Chengchi UniversityTaipei · Taiwan2017–2020 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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 · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 works

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

 Works by year

2020s

20262 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models (2025 · 157 citations)
    Reasoning-focused LLMs such as OpenAI o1 and DeepSeek-R1 get better at math and coding by writing long Chain-of-Thought traces.
  2. FaithLM: Towards Faithful Explanations for Large Language Models (2026 · 9 citations)
    LLMs often explain their answers in natural language, but these explanations frequently do not reflect what actually drove the prediction. Classic fidelity tests, which delete important tokens, also fit poorly with free-text rationales whose content is semantic rather than token-level.
  3. AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models (2026 · 7 citations)
    Distilling reasoning from strong teacher LLMs into non-reasoning students often causes "overthinking": the students produce very long chain-of-thought (CoT) traces even for easy questions, which raises decoding time and cost.

All works

4 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. Stop Overthinking: A Survey on Efficient Reasoning for Large Language ModelsarXiv:2503.16419Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.2025 · 157 citations
  2. FaithLM: Towards Faithful Explanations for Large Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, et al.2026 · 9 citations
  3. AutoL2S: Auto Long-Short Reasoning for Efficient Large Language ModelsFindings of the Association for Computational LinguisticsFeng Luo, Yu-Neng Chuang, Guanchu Wang, et al.2026 · 7 citations
  4. A Decoupled Multi-Agent Framework for Complex Text Style Transfer ↗Findings of the Association for Computational Linguistics: EMNLP 2025 · no summary yetLingxi Zhang et al.2025

 Co-authors here

12 of 16 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 168 times (of the 149,439 papers whose bibliographies we read); 68 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/yu-neng-chuang/