🧑‍🔬 Who cites Man Luo

136 citations of their works from 129 papers of our library (of the 149,439 whose bibliographies we read) · 21 with a page here, the most cited first · ← Man Luo

1. A Survey on In-context LearningarXiv:2301.00234Qingxiu Dong, Lei Li, Damai Dai, et al.2023 · 978 citations
2. A Survey on Data Selection for Language ModelsarXiv:2402.16827Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.2024 · 95 citations
3. BIG-Bench Extra HardAnnual Meeting of the Association for Computational Linguistics (ACL)Mehran Kazemi, Bahare Fatemi, Hritik Bansal, et al.2025 · 58 citations
4. Puzzle Solving using Reasoning of Large Language Models: A SurveyConference on Empirical Methods in Natural Language Processing (EMNLP)Panagiotis Giadikiaroglou, Maria Lymperaiou, Giorgos Filandrianos, et al.2024 · 48 citations
5. On Memorization of Large Language Models in Logical ReasoningInternational Joint Conference on Natural Language Processing (IJCNLP)Chulin Xie, Yangsibo Huang, Chiyuan Zhang, et al.2025 · 45 citations
6. Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Nisarg Patel, Mohith Kulkarni, Mihir Parmar, et al.2024 · 39 citations
7. Knowledge Boundary of Large Language Models: A SurveyAnnual Meeting of the Association for Computational Linguistics (ACL)Moxin Li, Yong Zhao, Wenxuan Zhang, et al.2025 · 30 citations
8. Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language ModelsarXiv:2310.00836Man Luo, Shrinidhi Kumbhar, Ming shen, et al.2023 · 27 citations
9. TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence ConfigurationConference on Empirical Methods in Natural Language Processing (EMNLP)Yanshu Li, Jianjiang Yang, Tian Yun, et al.2025 · 22 citations
10. Aristotle: Mastering Logical Reasoning with A Logic-Complete Decompose-Search-Resolve FrameworkAnnual Meeting of the Association for Computational Linguistics (ACL)Jundong Xu, Hao Fei, Meng Luo, et al.2025 · 16 citations
11. How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following BehaviorAnnual Meeting of the Association for Computational Linguistics (ACL)Zidi Xiong, Yuping Lin, Wenya Xie, et al.2026 · 13 citations
12. Aligning Large Language Models with Human Opinions through Persona Selection and Value — Belief — Norm ReasoningarXiv:2311.08385Do Xuan Long, Kenji Kawaguchi, Min-Yen Kan, et al.2023 · 12 citations
13. LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context ScenariosAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaodong Wu, Minhao Wang, Yichen Liu, et al.2025 · 11 citations
14. LogicPro: Improving Complex Logical Reasoning via Program-Guided LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Jin Jiang, Yuchen Yan, Yang Liu, et al.2025 · 8 citations
15. 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
16. What Breaks Knowledge Graph based RAG? Benchmarking and Empirical Insights into Reasoning under Incomplete KnowledgeConference of the European Chapter of the Association for Computational Linguistics (EACL)Dongzhuoran Zhou, Yuqicheng Zhu, Xiaxia Wang, et al.2026 · 3 citations
17. ReportLogic: Evaluating Logical Quality in Deep Research ReportsAnnual Meeting of the Association for Computational Linguistics (ACL)Jujia Zhao, Zhaoxin Huan, Zihan Wang, et al.2026 · 1 citation
18. CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor TrapsLanguage Resources and Evaluation Conference (LREC)Jungmin Yun, June Hyoung Kwon, Youngbin Kim2026
19. ExpressivityBench: Can LLMs Communicate Implicitly?Findings of the Association for Computational LinguisticsJoshua Tint, Som Sagar, Aditya Taparia, et al.2026
20. Good Reasoning Makes Good Demonstrations: Implicit Reasoning Quality Supervision via In-Context Reinforcement LearningFindings of the Association for Computational LinguisticsTiehua Mei, Minxuan Lv, Leiyu Pan, et al.2026
21. Truth or Sophistry? LoFa: A Benchmark for LLM Robustness Against Logical FallaciesAnnual Meeting of the Association for Computational Linguistics (ACL)Xudong Shen, li Yuan, Ye Chen, et al.2026

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