--- title: "Who cites Functional Benchmarks for — 32 citing papers" description: "32 papers of the Elamur Research library cite «Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning…" canonical: "https://research.elamur.ai/free/en/papers/srivastava-2024-functional-benchmarks-robust-evaluation-reasoning/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/srivastava-2024-functional-benchmarks-robust-evaluation-reasoning/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Functional Benchmarks for Robust Evaluation of…](https://md.research.elamur.ai/free/en/papers/srivastava-2024-functional-benchmarks-robust-evaluation-reasoning/) / cited by # Who cites «Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap» 32 papers of our library cite it (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · [← back to «Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap»](https://md.research.elamur.ai/free/en/papers/srivastava-2024-functional-benchmarks-robust-evaluation-reasoning/) 1. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context arXiv:2403.05530**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,589 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/) 2. [**GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models arXiv:2410.05229**Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, et al.*2024 · 273 citations*](https://md.research.elamur.ai/free/en/papers/mirzadeh-2024-gsm-symbolic-understanding-limitations-mathematical/) 3. **[A Careful Examination of Large Language Model Performance on Grade School Arithmetic](https://md.research.elamur.ai/free/en/papers/zhang-2024-careful-examination-large-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hugh Zhang, Jeff Da, Dean Lee, et al.*2024 · 101 citations* 4. [**Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A Survey arXiv:2404.01869**Philipp Mondorf, Barbara Plank*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/mondorf-2024-accuracy-evaluating-reasoning-behavior-large/) 5. **[On Memorization of Large Language Models in Logical Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2025-memorization-large-language-models-logical/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Chulin Xie, Yangsibo Huang, Chiyuan Zhang, et al.*2025 · 45 citations* 6. [**MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations arXiv:2502.06453**Kaixuan Huang, Jiacheng Guo, Zihao Li, et al.*2025 · 42 citations*](https://md.research.elamur.ai/free/en/papers/huang-2025-math-perturb-benchmarking-llms-math/) 7. [**Knowledge Distillation in Automated Annotation: Supervised Text Classification with LLM-Generated Training Labels arXiv:2406.17633**Nicholas Pangakis, Samuel Wolken*2024 · 42 citations*](https://md.research.elamur.ai/free/en/papers/pangakis-2024-knowledge-distillation-automated-annotation-supervised/) 8. [**DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models arXiv:2411.00836**Chengke Zou, Xingang Guo, Rui Yang, et al.*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/zou-2024-dynamath-dynamic-visual-benchmark-evaluating/) 9. **[Towards Robust Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/luong-2025-robust-mathematical-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Thang Luong, Dawsen Hwang, Hoang H Nguyen, et al.*2025 · 40 citations* 10. [**A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibility arXiv:2504.07086**Andreas Hochlehnert, Hardik Bhatnagar, Vishaal Udandarao, et al.*2025 · 36 citations*](https://md.research.elamur.ai/free/en/papers/hochlehnert-2025-sober-look-progress-language-model/) 11. **[MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark](https://md.research.elamur.ai/free/en/papers/zhao-2025-mmlu-cf-contamination-free-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qihao Zhao, Yangyu Huang, Tengchao Lv, et al.*2025 · 21 citations* 12. [**UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models arXiv:2502.00334**Xin Xu, Qiyun Xu, Tong Xiao, et al.*2025 · 19 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-ugphysics-comprehensive-benchmark-undergraduate-physics/) 13. **[EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving](https://md.research.elamur.ai/free/en/papers/zhou-2026-engibench-benchmark-evaluating-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiyuan Zhou, Xinlei Wang, Yirui He, et al.*2026 · 3 citations* 14. **[A Survey of Deep Learning for Geometry Problem Solving](https://md.research.elamur.ai/free/en/papers/ma-2026-survey-deep-learning-geometry-problem/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jianzhe Ma, Wenxuan Wang, Qin Jin*2026 · 1 citation* 18 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/srivastava-2024-functional-benchmarks-robust-evaluation-reasoning/) — Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap