Elamur Research⌕
☰
home / authors / Aykut Erdem / cited by / page 3

Who cites Aykut Erdem

1,279 citations of their works from 1,278 papers of our library (of the 149,439 whose bibliographies we read) · 386 with a page here, the most cited first · page 3 of 4 · 201–300 · ← Aykut Erdem

1 2 3 4

  1. Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language ModelsarXiv:2402.13064Haoran Li, Qingxiu Dong, Zhengyang Tang, et al.2024 · 65 citations
  2. Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic TasksarXiv:2305.14201Tiedong Liu, Bryan Kian Hsiang Low2023 · 65 citations
  3. Why think step by step? Reasoning emerges from the locality of experienceConference on Neural Information Processing Systems (NeurIPS)Ben Prystawski, Michael Y. Li, Noah D. Goodman2023 · 65 citations
  4. A Peek into Token Bias: Large Language Models Are Not Yet Genuine ReasonersConference on Empirical Methods in Natural Language Processing (EMNLP)Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, et al.2024 · 64 citations
  5. CValues: Measuring the Values of Chinese Large Language Models from Safety to ResponsibilityarXiv:2307.09705Guohai Xu, Jiayi Liu, Ming Yan, et al.2023 · 64 citations
  6. Leveraging Large Language Models for NLG Evaluation: Advances and ChallengesConference on Empirical Methods in Natural Language Processing (EMNLP)Zhen Li, Xiaohan Xu, Tao Shen, et al.2024 · 63 citations
  7. Exploring the Benefits of Training Expert Language Models over Instruction TuningarXiv:2302.03202Joel Jang, Seungone Kim, Seonghyeon Ye, et al.2023 · 63 citations
  8. Large Language Model Routing with Benchmark DatasetsarXiv:2309.15789Tal Shnitzer, Anthony Ou, Mirian Silva, et al.2023 · 63 citations
  9. LAB-Bench: Measuring Capabilities of Language Models for Biology ResearcharXiv:2407.10362Jon M. Laurent, Joseph D. Janizek, Michael Ruzo, et al.2024 · 62 citations
  10. Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use CasesarXiv:2303.14742Yunjie Ji, Yong Deng, Yan Gong, et al.2023 · 62 citations
  11. Rephrase and Respond: Let Large Language Models Ask Better Questions for ThemselvesarXiv:2311.04205Yihe Deng, Weitong Zhang, Zixiang Chen, et al.2023 · 62 citations
  12. SatLM: Satisfiability-Aided Language Models Using Declarative PromptingConference on Neural Information Processing Systems (NeurIPS)Xi Ye, Qiaochu Chen, Isil Dillig, et al.2023 · 62 citations
  13. Teaching Large Language Models to Reason with Reinforcement LearningarXiv:2403.04642Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, et al.2024 · 61 citations
  14. Large Language Models Are Not Strong Abstract ReasonersarXiv:2305.19555Gael Gendron, Qiming Bao, Michael Witbrock, et al.2023 · 61 citations
  15. BIG-Bench Extra HardAnnual Meeting of the Association for Computational Linguistics (ACL)Mehran Kazemi, Bahare Fatemi, Hritik Bansal, et al.2025 · 58 citations
  16. SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Yige Xu, Xu Guo, Zhiwei Zeng, et al.2025 · 58 citations
  17. Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling LawsarXiv:2401.00448Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.2024 · 57 citations
  18. SuperCLUE: A Comprehensive Chinese Large Language Model BenchmarkarXiv:2307.15020Liang Xu, Anqi Li, Lei Zhu, et al.2023 · 57 citations
  19. Teaching Arithmetic to Small TransformersarXiv:2307.03381Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, et al.2023 · 57 citations
  20. The Art of Saying No: Contextual Noncompliance in Language ModelsConference on Neural Information Processing Systems (NeurIPS)Faeze Brahman, Sachin Kumar, Vidhisha Balachandran, et al.2024 · 56 citations
  21. A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning ChainsAnnual Meeting of the Association for Computational Linguistics (ACL)Alon Jacovi, Yonatan Bitton, Bernd Bohnet, et al.2024 · 54 citations
  22. CMB: A Comprehensive Medical Benchmark in ChinesearXiv:2308.08833Xidong Wang, Guiming Hardy Chen, Dingjie Song, et al.2023 · 54 citations
  23. English Machine Reading Comprehension Datasets: A SurveyDaria Dzendzik, Jennifer Foster, Carl Vogel2021 · 54 citations
  24. Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?arXiv:2406.13121Jinhyuk Lee, Anthony Chen, Zhuyun Dai, et al.2024 · 53 citations
  25. RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and StylearXiv:2410.16184Yantao Liu, Zijun Yao, Rui Min, et al.2024 · 53 citations
  26. Language acquisition: do children and language models follow similar learning stages?arXiv:2306.03586Linnea Evanson, Yair Lakretz, Jean-Rémi King2023 · 53 citations
  27. On the application of Large Language Models for language teaching and assessment technologyarXiv:2307.08393Andrew Caines, Luca Benedetto, Shiva Taslimipoor, et al.2023 · 51 citations
  28. How to Train Data-Efficient LLMsarXiv:2402.09668Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, et al.2024 · 50 citations
  29. Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference FeedbackConference on Neural Information Processing Systems (NeurIPS)Hamish Ivison, Yizhong Wang, Jiacheng Liu, et al.2024 · 50 citations
  30. The Effectiveness of LLMs as Annotators: A Comparative Overview and Empirical Analysis of Direct RepresentationLanguage Resources and Evaluation Conference (LREC)Maja Pavlovic, Massimo Poesio2024 · 49 citations
  31. Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language ModelsarXiv:2406.02061Marianna Nezhurina, Lucia Cipolina-Kun, Mehdi Cherti, et al.2024 · 48 citations
  32. Linguistic ambiguity analysis in ChatGPTarXiv:2302.06426Miguel Ortega-Martin, Oscar Garcia-Sierra, Alfonso Ardoiz, et al.2023 · 48 citations
  33. Metaphor Understanding Challenge Dataset for LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaoyu Tong, Rochelle Choenni, Martha Lewis, et al.2024 · 47 citations
  34. Language models scale reliably with over-training and on downstream tasksarXiv:2403.08540Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, et al.2024 · 46 citations
  35. “According to . . . “: Prompting Language Models Improves Quoting from Pre-Training DataConference of the European Chapter of the Association for Computational Linguistics (EACL)Orion Weller, Marc Marone, Nathaniel Weir, et al.2024 · 46 citations
  36. GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local RefinementsarXiv:2402.10963Alex Havrilla, Sharath Raparthy, Christoforus Nalmpantis, et al.2024 · 45 citations
  37. Lost in Translation: Large Language Models in Non-English Content AnalysisarXiv:2306.07377Gabriel Nicholas, Aliya Bhatia2023 · 45 citations
  38. HyperCLOVA X Technical ReportarXiv:2404.01954Kang Min Yoo, Jaegeun Han, Sookyo In, et al.2024 · 44 citations
  39. Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare deliveryarXiv:2304.13714Debadutta Dash, Rahul Thapa, Juan M. Banda, et al.2023 · 44 citations
  40. On the Self-Verification Limitations of Large Language Models on Reasoning and Planning TasksarXiv:2402.08115Kaya Stechly, Karthik Valmeekam, Subbarao Kambhampati2024 · 42 citations
  41. Transcending Scaling Laws with 0.1% Extra ComputearXiv:2210.11399Yi Tay, Jason Wei, Hyung Won Chung, et al.2022 · 42 citations
  42. Auxiliary task demands mask the capabilities of smaller language modelsarXiv:2404.02418Jennifer Hu, Michael C. Frank2024 · 41 citations
  43. Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model CollaborationarXiv:2310.00280Qiushi Sun, Zhangyue Yin, Xiang Li, et al.2023 · 41 citations
  44. GPT Can Solve Mathematical Problems Without a CalculatorarXiv:2309.03241Zhen Yang, Ming Ding, Qingsong Lv, et al.2023 · 41 citations
  45. SALMON: Self-Alignment with Instructable Reward ModelsarXiv:2310.05910Zhiqing Sun, Yikang Shen, Hongxin Zhang, et al.2023 · 41 citations
  46. A Systematic Survey of Automatic Prompt Optimization TechniquesConference on Empirical Methods in Natural Language Processing (EMNLP)Kiran Ramnath, Kang Zhou, Sheng Guan, et al.2025 · 40 citations
  47. Observational Scaling Laws and the Predictability of Language Model PerformancearXiv:2405.10938Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto2024 · 40 citations
  48. Aligning Language Models with Preferences through f-divergence MinimizationarXiv:2302.08215Dongyoung Go, Tomasz Korbak, German Kruszewski, et al.2023 · 39 citations
  49. Introducing v0.5 of the AI Safety Benchmark from MLCommonsarXiv:2404.12241Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, et al.2024 · 38 citations
  50. Aligning Large Language Models with Human Preferences through Representation EngineeringAnnual Meeting of the Association for Computational Linguistics (ACL)Wenhao Liu, Xiaohua Wang, Muling Wu, et al.2024 · 37 citations
  51. CITE: A Corpus of Image-Text Discourse RelationsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Malihe Alikhani, Sreyasi Nag Chowdhury, Gerard de Melo, et al.2019 · 37 citations
  52. Measuring AI Ability to Complete Long Software TasksConference on Neural Information Processing Systems (NeurIPS)Thomas Kwa, Ben West, Joel Becker, et al.2025 · 36 citations
  53. The Surprising Effectiveness of Test-Time Training for Few-Shot LearningarXiv:2411.07279Ekin Akyürek, Mehul Damani, Adam Zweiger, et al.2024 · 36 citations
  54. AbstentionBench: Reasoning LLMs Fail on Unanswerable QuestionsConference on Neural Information Processing Systems (NeurIPS)Polina Kirichenko, Mark Ibrahim, Kamalika Chaudhuri, et al.2025 · 35 citations
  55. LAiW: A Chinese Legal Large Language Models BenchmarkInternational Conference on Computational Linguistics (COLING)Yongfu Dai, Duanyu Feng, Jimin Huang, et al.2025 · 35 citations
  56. Through the Lens of Core Competency: Survey on Evaluation of Large Language ModelsarXiv:2308.07902Ziyu Zhuang, Qiguang Chen, Longxuan Ma, et al.2023 · 35 citations
  57. Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language ModelsarXiv:2411.07140Yancheng He, Shilong Li, Jiaheng Liu, et al.2024 · 34 citations
  58. ChessGPT: Bridging Policy Learning and Language ModelingConference on Neural Information Processing Systems (NeurIPS)Xidong Feng, Yicheng Luo, Ziyan Wang, et al.2023 · 34 citations
  59. M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language ModelsarXiv:2305.10263Chuang Liu, Renren Jin, Yuqi Ren, et al.2023 · 34 citations
  60. Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge EvaluationarXiv:2306.05783Zhouhong Gu, Xiaoxuan Zhu, Haoning Ye, et al.2023 · 34 citations
  61. SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model SafetyarXiv:2404.05399Paul Rottger, Fabio Pernisi, Bertie Vidgen, et al.2024 · 33 citations
  62. BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language ModelsarXiv:2309.06085Wei Qi Leong, Jian Gang Ngui, Yosephine Susanto, et al.2023 · 33 citations
  63. The Life Cycle of Knowledge in Big Language Models: A SurveyarXiv:2303.07616 · Machine Intelligence Research. vol. 21, no. 2, pp. 217-238, 2024Boxi Cao, Hongyu Lin, Xianpei Han, et al.2023 · 31 citations
  64. Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM EvaluationInternational Conference on Computational Linguistics (COLING)Siyuan Wang, Zhuohan Long, Zhihao Fan, et al.2025 · 30 citations
  65. DsDm: Model-Aware Dataset Selection with DatamodelsarXiv:2401.12926Logan Engstrom, Axel Feldmann, Aleksander Madry2024 · 30 citations
  66. The Quantization Model of Neural ScalingarXiv:2303.13506Eric J. Michaud, Ziming Liu, Uzay Girit, et al.2023 · 30 citations
  67. Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM SamplingConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Guangya Wan, Yuqi Wu, Jie Chen, et al.2025 · 29 citations
  68. One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial SupportarXiv:2306.09237Ronja Stern, Vishvaksenan Rasiah, Veton Matoshi, et al.2023 · 29 citations
  69. Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model PerformanceConference on Empirical Methods in Natural Language Processing (EMNLP)Omer Nahum, Nitay Calderon, Orgad Keller, et al.2025 · 27 citations
  70. The Task Shield: Enforcing Task Alignment to Defend Against Indirect Prompt Injection in LLM AgentsAnnual Meeting of the Association for Computational Linguistics (ACL)Feiran Jia, Tong Wu, Xin Qin, et al.2025 · 27 citations
  71. Towards the Law of Capacity Gap in Distilling Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Chen Zhang, Qiuchi Li, Dawei Song, et al.2025 · 27 citations
  72. MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical ReasoningConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Debrup Das, Debopriyo Banerjee, Somak Aditya, et al.2024 · 27 citations
  73. Prediction of Arabic Legal Rulings using Large Language ModelsElectronicsAdel Ammar, Anis Koubaa, Bilel Benjdira, et al.2023 · 27 citations
  74. 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
  75. Feedback Loops With Language Models Drive In-Context Reward HackingarXiv:2402.06627Alexander Pan, Erik Jones, Meena Jagadeesan, et al.2024 · 26 citations
  76. Construction Grammar Provides Unique Insight into Neural Language ModelsInternational Workshop on Construction Grammars and NLPLeonie Weissweiler, Taiqi He, Naoki Otani, et al.2023 · 26 citations
  77. Logical Reasoning in Large Language Models: A SurveyarXiv:2502.09100Hanmeng Liu, Zhizhang Fu, Mengru Ding, et al.2025 · 25 citations
  78. IberoBench: A Benchmark for LLM Evaluation in Iberian LanguagesInternational Conference on Computational Linguistics (COLING)Irene Baucells, Javier Aula-Blasco, Iria de-Dios-Flores, et al.2025 · 24 citations
  79. MiMo-Audio: Audio Language Models are Few-Shot LearnersarXiv:2512.23808Xiaomi LLM-Core Team, Dong Zhang, Gang Wang, et al.2025 · 24 citations
  80. From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction TuningarXiv:2304.07995Qian Liu, Fan Zhou, Zhengbao Jiang, et al.2023 · 24 citations
  81. Symbolic Regression with a Learned Concept LibraryConference on Neural Information Processing Systems (NeurIPS)Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes, et al.2024 · 23 citations
  82. COLLIE: Systematic Construction of Constrained Text Generation TasksarXiv:2307.08689Shunyu Yao, Howard Chen, Austin W. Hanjie, et al.2023 · 23 citations
  83. Joint Prompt Optimization of Stacked LLMs using Variational InferenceConference on Neural Information Processing Systems (NeurIPS)Alessandro Sordoni, Xingdi Yuan, Marc-Alexandre Cote, et al.2023 · 23 citations
  84. CodeI/O: Condensing Reasoning Patterns via Code Input-Output PredictionarXiv:2502.07316Junlong Li, Daya Guo, Dejian Yang, et al.2025 · 21 citations
  85. Language Models as Models of LanguagearXiv:2408.07144Raphael Milliere2024 · 21 citations
  86. Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal ReasoningarXiv:2310.03249Mohamed Aghzal, Erion Plaku, Ziyu Yao2023 · 21 citations
  87. DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AIFindings of the Association for Computational LinguisticsJianguo Zhang, Kun Qian, Zhiwei Liu, et al.2023 · 21 citations
  88. MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for ReasoningConference on Empirical Methods in Natural Language Processing (EMNLP)Justin Chen, Archiki Prasad, Swarnadeep Saha, et al.2025 · 20 citations
  89. The Art of Scaling Reinforcement Learning Compute for LLMsarXiv:2510.13786Devvrit Khatri, Lovish Madaan, Rishabh Tiwari, et al.2025 · 20 citations
  90. The Vector Grounding ProblemarXiv:2304.01481Dimitri Coelho Mollo, Raphael Milliere2023 · 20 citations
  91. How predictable is language model benchmark performance?arXiv:2401.04757David Owen2024 · 19 citations
  92. Ecologically Valid Explanations for Label Variation in NLIFindings of the Association for Computational LinguisticsNan-Jiang Jiang, Chenhao Tan, Marie-Catherine de Marneffe2023 · 19 citations
  93. RECKONING: Reasoning through Dynamic Knowledge EncodingConference on Neural Information Processing Systems (NeurIPS)Zeming Chen, Gail Weiss, Eric Mitchell, et al.2023 · 19 citations
  94. Do Large Language Models Understand Word Senses?Domenico Meconi, Simone Stirpe, Federico Martelli2025 · 18 citations
  95. Aviary: training language agents on challenging scientific tasksarXiv:2412.21154Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths, et al.2024 · 18 citations
  96. Large Language Model ProgramsarXiv:2305.05364Imanol Schlag, Sainbayar Sukhbaatar, Asli Celikyilmaz, et al.2023 · 18 citations
  97. Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMsarXiv:2507.09477Yangning Li, Weizhi Zhang, Yuyao Yang, et al.2025 · 17 citations
  98. ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of EventsAnnual Meeting of the Association for Computational Linguistics (ACL)Duygu Sezen Islakoglu, Jan-Christoph Kalo2025 · 16 citations
  99. Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle ChallengesInternational Conference on Computational Linguistics (COLING)Vinay Samuel, Yue Zhou, Henry Peng Zou2025 · 16 citations
  100. The "LLM World of Words" English free association norms generated by large language modelsScientific DataKatherine Abramski, Riccardo Improta, Giulio Rossetti, et al.2024 · 16 citations

1 2 3 4

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/aykut-erdem/cited-by/page/3/