🧑‍🔬 Who cites Mizanur Rahman

150 citations of their works from 140 papers of our library (of the 149,439 whose bibliographies we read) · 34 with a page here, the most cited first · ← Mizanur Rahman

1. A Survey on Evaluation of Large Language ModelsACM Transactions on Intelligent Systems and TechnologyYupeng Chang, Xu Wang, Jindong Wang, et al.2023 · 3,648 citations
2. A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and InteractivityarXiv:2302.04023Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, et al.2023 · 882 citations
3. TrustLLM: Trustworthiness in Large Language ModelsarXiv:2401.05561Yue Huang, Lichao Sun, Haoran Wang, et al.2024 · 237 citations
4. Orca 2: Teaching Small Language Models How to ReasonarXiv:2311.11045Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.2023 · 162 citations
5. ALLaM: Large Language Models for Arabic and EnglisharXiv:2407.15390M Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani, et al.2024 · 113 citations
6. GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLPConference on Empirical Methods in Natural Language Processing (EMNLP)Md Tawkat Islam Khondaker, Abdul Waheed, El Moatez Billah Nagoudi, et al.2023 · 110 citations
7. ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language ModelsLanguage Resources and Evaluation Conference (LREC)Ning Bian, Xianpei Han, Le Sun, et al.2024 · 106 citations
8. A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and RecommendationsConference on Empirical Methods in Natural Language Processing (EMNLP)Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, et al.2024 · 105 citations
9. FLASK: Fine-grained Language Model Evaluation based on Alignment Skill SetsarXiv:2307.10928Seonghyeon Ye, Doyoung Kim, Sungdong Kim, et al.2023 · 100 citations
10. GPTEval: A Survey on Assessments of ChatGPT and GPT-4Language Resources and Evaluation Conference (LREC)Rui Mao, Guanyi Chen, Xulang Zhang, et al.2024 · 75 citations
11. When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model LeaderboardsarXiv:2402.01781Norah Alzahrani, Hisham Abdullah Alyahya, Yazeed Alnumay, et al.2024 · 74 citations
12. CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model GenerationarXiv:2311.18702Pei Ke, Bosi Wen, Zhuoer Feng, et al.2023 · 69 citations
13. ARB: Advanced Reasoning Benchmark for Large Language ModelsarXiv:2307.13692Tomohiro Sawada, Daniel Paleka, Alexander Havrilla, et al.2023 · 66 citations
14. TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic VarietiesArabic Natural Language Processing Conference (ArabicNLP)Karima Kadaoui, Samar M. Magdy, Abdul Waheed, et al.2023 · 60 citations
15. The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Nitay Calderon, Roi Reichart, Rotem Dror2025 · 52 citations
16. CFBench: A Comprehensive Constraints-Following Benchmark for LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Tao Zhang, ChengLIn Zhu, Yanjun Shen, et al.2025 · 30 citations
17. PPTAgent: Generating and Evaluating Presentations Beyond Text-to-SlidesConference on Empirical Methods in Natural Language Processing (EMNLP)Hao Zheng, Xinyan Guan, Hao Kong, et al.2025 · 30 citations
18. Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the AboveAnnual Meeting of the Association for Computational Linguistics (ACL)Nishant Balepur, Rachel Rudinger, Jordan Boyd-Graber2025 · 28 citations
19. New Trends for Modern Machine Translation with Large Reasoning ModelsLanguage Resources and Evaluation Conference (LREC)Sinuo Liu, Chenyang Lyu, Minghao Wu, et al.2026 · 16 citations
20. Benchmarking LLaMA-3 on Arabic Language Generation TasksArabic Natural Language Processing Conference (ArabicNLP)Md Tawkat Islam Khondaker, Numaan Naeem, Fatimah Khan, et al.2024 · 12 citations
21. LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Ming Zhang, Yujiong Shen, Jingyi Deng, et al.2026 · 3 citations
22. Adaptive Data Flywheel: Applying MAPE Control Loops to AI Agent ImprovementConference of the European Chapter of the Association for Computational Linguistics (EACL)Aaditya Shukla, Sidney Knowles, Meenakshi Madugula, et al.2026 · 1 citation
23. Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Wenxuan Wang, Zizhan Ma, Guo Yu, et al.2026 · 1 citation
24. AraEval: An Arabic Multi-Task Evaluation Suite for Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Alhanoof Althnian, Norah A. Alzahrani, Shaykhah Z. Alsubaie, et al.2025 · 1 citation
25. Evaluation of Large Language Models on Arabic Punctuation PredictionWorkshop on NLP for Languages Using Arabic ScriptAsma Ali Al Wazrah, Afrah Altamimi, Hawra Aljasim, et al.2025 · 1 citation
26. Aligning Text, Code, and Vision: A Multi-Objective Reinforcement Learning Framework for Text-to-VisualizationConference of the European Chapter of the Association for Computational Linguistics (EACL)Mizanur Rahman, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, et al.2026
27. Benchmark Data Contamination in Underrepresented Languages: A Comprehensive Analysis Using Brazilian DataLanguage Resources and Evaluation Conference (LREC)Iriedson Souto Maior de Moraes Vilar, David Candeia Maia, João Brunet, et al.2026
28. Charting the European LLM Benchmarking Landscape: A New Taxonomy and RegistryShaping Multilingual, Multimodal AI for the Social Sciences and Humanities @ LRECSpela Vintar, Mojca Brglez, Taja Kuzman Pungeršek, et al.2026
29. ExpressivityBench: Can LLMs Communicate Implicitly?Findings of the Association for Computational LinguisticsJoshua Tint, Som Sagar, Aditya Taparia, et al.2026
30. Identifying the Achilles' Heel: An Iterative Method for Uncovering Factual Errors in Large Language ModelsFindings of the Association for Computational LinguisticsWenxuan Wang, Yuk-Kit Chan, Zixuan Ling, et al.2026
31. The Relevance of Value Systems for Offensive Language DetectionConference of the European Chapter of the Association for Computational Linguistics (EACL)Michael Wiegand, Elisabeth Eder, Josef Ruppenhofer2026
32. Who Generates More Empathetic Responses–-Humans or LLMs? A Comparative Evaluation with Human and LLM JudgesConference on Computational Natural Language Learning (CoNLL)Anuradha Welivita, Fawzia Zeitoun, Pearl Pu2026
33. The Need for Robust and Inclusive Benchmarks in Evaluating LLMs on Arabic TextInternational Conference on Natural Language and Speech ProcessingLubana Al Rayes, Ashraf Elnagar2025
34. Complexity factors of language models in reading comprehension: a controlled experiment (Étude des facteurs de complexité des modèles de langage dans une tâche de compréhension de lecture à l'aide d'une expérience contrôlée sémantiquement)JEP-TALN-RECITAL (the French-speaking conferences on speech and natural language processing)Elie Antoine, Frederic Bechet, Géraldine Damnati, et al.2024

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