🧑‍🔬 Who cites Guangya Wan

28 citations of their works from 27 papers of our library (of the 149,439 whose bibliographies we read) · 12 with a page here, the most cited first · ← Guangya Wan

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. Efficient Reasoning Models: A SurveyarXiv:2504.10903Sicheng Feng, Gongfan Fang, Xinyin Ma, et al.2025 · 37 citations
3. Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language ModelsarXiv:2503.24377Rui Wang, Hongru Wang, Boyang Xue, et al.2025 · 16 citations
4. Trans-EnV: A Framework for Evaluating the Linguistic Robustness of LLMs Against English VarietiesConference on Neural Information Processing Systems (NeurIPS)Jiyoung Lee, Seungho Kim, Jieun Han, et al.2025 · 3 citations
5. The Hrunting of AI: Where and How to Improve English Dialectal FairnessarXiv:2603.15187Wei Li, Adrian de Wynter2026 · 1 citation
6. ThinkBrake: Efficient Reasoning via Log-Probability Margin Guided DecodingFindings of the Association for Computational LinguisticsSangjun Song, Minjae Oh, Seungkyu Lee, et al.2026 · 1 citation
7. Confidence-Weighted Token Set Cover for Early Hypothesis Pruning in Self-ConsistencyFindings of the Association for Computational LinguisticsMd Arafat Sultan, Ramón Fernandez Astudillo2026
8. DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English DialectsAnnual Meeting of the Association for Computational Linguistics (ACL)Jason S Lucas, Matt Murtagh White, Ali Al-Lawati, et al.2026
9. Evaluating the Usage of African-American Vernacular English in Large Language ModelsarXiv:2602.21485Deja Dunlap, R. Thomas McCoy2026
10. Individual and Combined Effects of English as a Second Language and Typos on LLM PerformancearXiv:2604.04723Serena Liu; Yutong Yang; Prisha Sheth; Weixuan Dong; Mingjiao Diao; Xinru Zhu; Nikhil Banga; Oscar Melendez; Arnav Sharma; Minda Zhao; Marina Lin;, Mengyu Wang2026
11. Self-Sum: Teaching an Agent to Decide Itself When and What to SummarizeFindings of the Association for Computational LinguisticsHongru Wang, Rui Wang, Jushi Kai, et al.2026
12. Side-by-side Comparison Amplifies Dialect Bias in Language ModelsACM Conference on Fairness, Accountability, and Transparency (FAccT)Kritee Kondapally, Claire J. Smerdon, Pooja C. Patel, et al.2026

15 more citing papers of our library have no page here yet.

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