Chengyu Huang
Chengyu Huang has 2 papers in our library, in 2026, most of them on Language models & pretraining, Reasoning; often with Claire Cardie, Zhengxin Zhang, Dan Zhao.
OpenAlex · ORCID · Wikidata · h-index 35 · 108 works · 4,331 citations OpenAlex, 2026-10-14
📄 2 works in our library · 🔗 Cited 3 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #12196
Affiliations
Fields, topics and concepts
📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works
🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 2 works
Works by year
2020s
Papers
- Better LLM Reasoning via Dual-Play (2026 · 3 citations)
Reinforcement learning with verifiable rewards (RLVR) has improved LLM reasoning, but it depends on large sets of curated problems with correct answers. - GSM-Noise: Exploring and Enhancing Large Language Models' Reasoning under Noisy Inputs (2026)
LLMs that score well on clean math benchmarks often fail when real users send messy prompts. The authors first examined long English prompts from WildChat-1M, using QwQ-32B as a checker. Grammar slips, symbol errors, misordered statements and irrelevant content were all common, and longer prompts contained more of…
All works
- Matrix-transmitted paratensile signaling enables myofibroblast – fibroblast cross talk in fibrosis expansion ↗Proceedings of the National Academy of Sciences · no summary yetLongwei Liu et al.2020 · 93 citations
- Better LLM Reasoning via Dual-PlayFindings of the Association for Computational LinguisticsZhengxin Zhang, Chengyu Huang, Aochong Oliver Li, et al.2026 · 3 citations
- GSM-Noise: Exploring and Enhancing Large Language Models' Reasoning under Noisy InputsFindings of the Association for Computational LinguisticsZhengxin Zhang, Chengyu Huang, Xufu Liu, et al.2026
Co-authors here
Venues
- Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026 ×2
- Proceedings of the National Academy of Sciences
Cited by
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