--- title: "Mathematics & statistics — papers, page 4" description: "Mathematics & statistics: 161 papers of the Elamur Research library · page 4 of 7 — our summary of each, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/shelves/mathematics/page/4/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/mathematics/page/4/) [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 4 # ➗ Mathematics & statistics 161 papers · page 4 of 7 · newest 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/page/3/) [1](https://md.research.elamur.ai/free/en/shelves/mathematics/) [2](https://md.research.elamur.ai/free/en/shelves/mathematics/page/2/) [3](https://md.research.elamur.ai/free/en/shelves/mathematics/page/3/) **4** [5](https://md.research.elamur.ai/free/en/shelves/mathematics/page/5/) [6](https://md.research.elamur.ai/free/en/shelves/mathematics/page/6/) [7](https://md.research.elamur.ai/free/en/shelves/mathematics/page/7/) [next →](https://md.research.elamur.ai/free/en/shelves/mathematics/page/5/) - [Let's Verify Step by Step](https://md.research.elamur.ai/free/en/papers/lightman-2023-let-s-verify-step-step/) — Hunter Lightman, Vineet Kosaraju, Yura Burda, et al. · 2023 - [LEVER: Learning to Verify Language-to-Code Generation with Execution](https://md.research.elamur.ai/free/en/papers/ni-2023-lever-learning-verify-language-code/) — Ansong Ni, Srini Iyer, Dragomir Radev, et al. · 2023 - [LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers](https://md.research.elamur.ai/free/en/papers/olausson-2023-linc-neurosymbolic-approach-logical-reasoning/) — Theo X. Olausson, Alex Gu, Benjamin Lipkin, et al. · 2023 - [Llemma: An Open Language Model For Mathematics](https://md.research.elamur.ai/free/en/papers/azerbayev-2023-llemma-open-language-model-mathematics/) — Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, et al. · 2023 - [Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning](https://md.research.elamur.ai/free/en/papers/pan-2023-logic-lm-empowering-large-language/) — Liangming Pan, Alon Albalak, Xinyi Wang, et al. · 2023 - [Making Language Models Better Reasoners with Step-Aware Verifier](https://md.research.elamur.ai/free/en/papers/li-2023-making-language-models-better-reasoners/) — Yifei Li, Zeqi Lin, Shizhuo Zhang, et al. · 2023 - [MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning](https://md.research.elamur.ai/free/en/papers/yue-2023-mammoth-building-math-generalist-models/) — Xiang Yue, Xingwei Qu, Ge Zhang, et al. · 2023 - [MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2023-mathcoder-seamless-code-integration-llms/) — Ke Wang, Houxing Ren, Aojun Zhou, et al. · 2023 - [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 - [MathPrompter: Mathematical Reasoning using Large Language Models](https://md.research.elamur.ai/free/en/papers/imani-2023-mathprompter-mathematical-reasoning-large-language/) — Shima Imani, Liang Du, Harsh Shrivastava · 2023 - [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 - [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 - [MiniLLM: On-Policy Distillation of Large Language Models](https://md.research.elamur.ai/free/en/papers/gu-2023-minillm-policy-distillation-large-language/) — Yuxian Gu, Li Dong, Furu Wei, et al. · 2023 - [NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark](https://md.research.elamur.ai/free/en/papers/sainz-2023-nlp-evaluation-trouble-need-measure/) — Oscar Sainz, Jon Campos, Iker García-Ferrero, et al. · 2023 - [On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes](https://md.research.elamur.ai/free/en/papers/agarwal-2023-policy-distillation-language-models-learning/) — Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, et al. · 2023 - [OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text](https://md.research.elamur.ai/free/en/papers/paster-2023-openwebmath-open-dataset-high-quality/) — Keiran Paster, Marco Dos Santos, Zhangir Azerbayev, et al. · 2023 - [Orca 2: Teaching Small Language Models How to Reason](https://md.research.elamur.ai/free/en/papers/mitra-2023-orca-2-teaching-small-language/) — Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al. · 2023 - [Orca: Progressive Learning from Complex Explanation Traces of GPT-4](https://md.research.elamur.ai/free/en/papers/mukherjee-2023-orca-progressive-learning-complex-explanation/) — Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, et al. · 2023 - [Progress measures for grokking via mechanistic interpretability](https://md.research.elamur.ai/free/en/papers/nanda-2023-progress-measures-grokking-mechanistic-interpretability/) — Neel Nanda, Lawrence Chan, Tom Lieberum, et al. · 2023 - [ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics](https://md.research.elamur.ai/free/en/papers/azerbayev-2023-proofnet-autoformalizing-formally-proving-undergraduate/) — Zhangir Azerbayev, Bartosz Piotrowski, Hailey Schoelkopf, et al. · 2023 - [Reinforced Self-Training (ReST) for Language Modeling](https://md.research.elamur.ai/free/en/papers/gulcehre-2023-reinforced-self-training-rest-language/) — Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, et al. · 2023 - [Rethinking Benchmark and Contamination for Language Models with Rephrased Samples](https://md.research.elamur.ai/free/en/papers/yang-2023-rethinking-benchmark-contamination-language-models/) — Shuo Yang, Wei-Lin Chiang, Lianmin Zheng, et al. · 2023 - [Scaling Relationship on Learning Mathematical Reasoning with Large Language Models](https://md.research.elamur.ai/free/en/papers/yuan-2023-scaling-relationship-learning-mathematical-reasoning/) — Zheng Yuan, Hongyi Yuan, Chengpeng Li, et al. · 2023 - [SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2023-scibench-evaluating-college-level-scientific/) — Xiaoxuan Wang, Ziniu Hu, Pan Lu, et al. · 2023 - [SCOTT: Self-Consistent Chain-of-Thought Distillation](https://md.research.elamur.ai/free/en/papers/wang-2023-scott-self-consistent-chain-thought/) — Peifeng Wang, Zhengyang Wang, Zheng Li, et al. · 2023 [← prev](https://md.research.elamur.ai/free/en/shelves/mathematics/page/3/) [1](https://md.research.elamur.ai/free/en/shelves/mathematics/) [2](https://md.research.elamur.ai/free/en/shelves/mathematics/page/2/) [3](https://md.research.elamur.ai/free/en/shelves/mathematics/page/3/) **4** [5](https://md.research.elamur.ai/free/en/shelves/mathematics/page/5/) [6](https://md.research.elamur.ai/free/en/shelves/mathematics/page/6/) [7](https://md.research.elamur.ai/free/en/shelves/mathematics/page/7/) [next →](https://md.research.elamur.ai/free/en/shelves/mathematics/page/5/)