--- title: "Mathematics & statistics — papers, page 2" description: "Mathematics & statistics: 166 papers of the Elamur Research library · page 2 of 7 — our summary of each, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/shelves/mathematics/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/mathematics/page/2/) [home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / [Mathematics & statistics](https://md.research.elamur.ai/free/en/shelves/mathematics/) / page 2 # ➗ Mathematics & statistics 166 papers · page 2 of 7 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/) Mathematics, proofs, computational mathematics, word problems and how machines learn to solve them, statistics. [← prev](https://md.research.elamur.ai/free/en/shelves/mathematics/) [1](https://md.research.elamur.ai/free/en/shelves/mathematics/) **2** [3](https://md.research.elamur.ai/free/en/shelves/mathematics/page/3/) … [7](https://md.research.elamur.ai/free/en/shelves/mathematics/page/7/) [next →](https://md.research.elamur.ai/free/en/shelves/mathematics/page/3/) - [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/) — Dheeru Dua, Yizhong Wang, Pradeep Dasigi, et al. · 2019 · 941 citations - [Are NLP Models really able to Solve Simple Math Word Problems?](https://md.research.elamur.ai/free/en/papers/patel-2021-are-nlp-models-really-able/) — Arkil Patel, Satwik Bhattamishra, Navin Goyal · 2021 · 882 citations - [STaR: Bootstrapping Reasoning With Reasoning](https://md.research.elamur.ai/free/en/papers/zelikman-2022-star-bootstrapping-reasoning-reasoning/) — Eric Zelikman, Yuhuai Wu, Jesse Mu, et al. · 2022 · 743 citations - [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://md.research.elamur.ai/free/en/papers/wang-2024-mmlu-pro-more-robust-challenging/) — Yubo Wang, Xueguang Ma, Ge Zhang, et al. · 2024 · 731 citations - [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://md.research.elamur.ai/free/en/papers/xu-2023-wizardlm-empowering-large-pre-trained/) — Can Xu, Qingfeng Sun, Kai Zheng, et al. · 2023 · 729 citations - [Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes](https://md.research.elamur.ai/free/en/papers/hsieh-2023-distilling-step-step-outperforming-larger/) — Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, et al. · 2023 · 684 citations - [DAPO: An Open-Source LLM Reinforcement Learning System at Scale](https://md.research.elamur.ai/free/en/papers/yu-2025-dapo-open-source-llm-reinforcement/) — Qiying Yu, Zheng Zhang, Ruofei Zhu, et al. · 2025 · 634 citations - [s1: Simple test-time scaling](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/) — Niklas Muennighoff, Zitong Yang, Weijia Shi, et al. · 2025 · 584 citations - [Solving General Arithmetic Word Problems](https://md.research.elamur.ai/free/en/papers/roy-2015-solving-general-arithmetic-word-problems/) — Subhro Roy, Dan Roth · 2015 · 580 citations - [AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models](https://md.research.elamur.ai/free/en/papers/zhong-2024-agieval-human-centric-benchmark-evaluating/) — Wanjun Zhong, Ruixiang Cui, Yiduo Guo, et al. · 2024 · 563 citations - [Galactica: A Large Language Model for Science](https://md.research.elamur.ai/free/en/papers/taylor-2022-galactica-large-language-model-science/) — Ross Taylor, Marcin Kardas, Guillem Cucurull, et al. · 2022 · 558 citations - [MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models](https://md.research.elamur.ai/free/en/papers/yu-2023-metamath-bootstrap-your-own-mathematical/) — Longhui Yu, Weisen Jiang, Han Shi, et al. · 2023 · 556 citations - [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://md.research.elamur.ai/free/en/papers/shi-2023-large-language-models-easily-distracted/) — Freda Shi, Xinyun Chen, Kanishka Misra, et al. · 2023 · 525 citations - [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://md.research.elamur.ai/free/en/papers/snell-2024-scaling-llm-test-time-compute/) — Charlie Snell, Jaehoon Lee, Kelvin Xu, et al. · 2024 · 524 citations - [Learning to Solve Arithmetic Word Problems with Verb Categorization](https://md.research.elamur.ai/free/en/papers/hosseini-2014-learning-solve-arithmetic-word-problems/) — Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, et al. · 2014 · 513 citations - [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://md.research.elamur.ai/free/en/papers/chen-2021-finqa-dataset-numerical-reasoning-over/) — Zhiyu Chen, Wenhu Chen, Charese Smiley, et al. · 2021 · 510 citations - [MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms](https://md.research.elamur.ai/free/en/papers/amini-2019-mathqa-interpretable-math-word-problem/) — Aida Amini, Saadia Gabriel, Shanchuan Lin, et al. · 2019 · 500 citations - [Language Models are Multilingual Chain-of-Thought Reasoners](https://md.research.elamur.ai/free/en/papers/shi-2022-language-models-multilingual-chain-thought/) — Freda Shi, Mirac Suzgun, Markus Freitag, et al. · 2022 · 490 citations - [Phi-4 Technical Report](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-4-technical-report/) — Marah Abdin, Jyoti Aneja, Harkirat Behl, et al. · 2024 · 489 citations - [Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations](https://md.research.elamur.ai/free/en/papers/wang-2024-math-shepherd-verify-reinforce-llms/) — Peiyi Wang, Lei Li, Zhihong Shao, et al. · 2024 · 482 citations - [Solving olympiad geometry without human demonstrations](https://md.research.elamur.ai/free/en/papers/trinh-2024-solving-olympiad-geometry-without-human/) — Trieu H. Trinh, Yuhuai Wu, Quoc V. Le, et al. · 2024 · 482 citations - [OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems](https://md.research.elamur.ai/free/en/papers/he-2024-olympiadbench-challenging-benchmark-promoting-agi/) — Chaoqun He, Renjie Luo, Yuzhuo Bai, et al. · 2024 · 448 citations - [Show your work: Scratchpads for intermediate computation with language models](https://md.research.elamur.ai/free/en/papers/al-2021-show-your-work-scratchpads-intermediate/) — 2021 · 448 citations - [Mathematical Capabilities of ChatGPT](https://md.research.elamur.ai/free/en/papers/frieder-2023-mathematical-capabilities-chatgpt/) — Simon Frieder, Luca Pinchetti, Alexis Chevalier, et al. · 2023 · 443 citations - [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://md.research.elamur.ai/free/en/papers/lu-2023-mathvista-evaluating-mathematical-reasoning-foundation/) — Pan Lu, Hritik Bansal, Tony Xia, et al. · 2023 · 441 citations [← prev](https://md.research.elamur.ai/free/en/shelves/mathematics/) [1](https://md.research.elamur.ai/free/en/shelves/mathematics/) **2** [3](https://md.research.elamur.ai/free/en/shelves/mathematics/page/3/) … [7](https://md.research.elamur.ai/free/en/shelves/mathematics/page/7/) [next →](https://md.research.elamur.ai/free/en/shelves/mathematics/page/3/)