--- title: "Mathematics & statistics — papers, page 6" description: "Mathematics & statistics: 161 papers of the Elamur Research library · page 6 of 7 — our summary of each, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/shelves/mathematics/page/6/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/mathematics/page/6/) [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 6 # ➗ Mathematics & statistics 161 papers · page 6 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/5/) [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](https://md.research.elamur.ai/free/en/shelves/mathematics/page/4/) [5](https://md.research.elamur.ai/free/en/shelves/mathematics/page/5/) **6** [7](https://md.research.elamur.ai/free/en/shelves/mathematics/page/7/) [next →](https://md.research.elamur.ai/free/en/shelves/mathematics/page/7/) - [Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/) — Takeshi Kojima, Shixiang Shane Gu, Machel Reid, et al. · 2022 - [Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction](https://md.research.elamur.ai/free/en/papers/jie-2022-learning-reason-deductively-math-word/) — Zhanming Jie, Jierui Li, Wei Lu · 2022 - [Least-to-most prompting enables complex reasoning in large language models](https://md.research.elamur.ai/free/en/papers/al-2022-least-most-prompting-enables-complex/) — 2022 - [LILA: A Unified Benchmark for Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/mishra-2022-lila-unified-benchmark-mathematical-reasoning/) — Swaroop Mishra, Matthew Finlayson, Pan Lu, et al. · 2022 - [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/) — Xuezhi Wang, Jason Wei, Dale Schuurmans, et al. · 2022 - [Solving math word problems with process- and outcome-based feedback](https://md.research.elamur.ai/free/en/papers/uesato-2022-solving-math-word-problems-process/) — Jonathan Uesato, Nate Kushman, Ramana Kumar, et al. · 2022 - [Solving Quantitative Reasoning Problems with Language Models](https://md.research.elamur.ai/free/en/papers/lewkowycz-2022-solving-quantitative-reasoning-problems-language/) — Aitor Lewkowycz, Anders Andreassen, David Dohan, et al. · 2022 - [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 - [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 - [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 - [Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning](https://md.research.elamur.ai/free/en/papers/lu-2021-inter-gps-interpretable-geometry-problem/) — Pan Lu, Ran Gong, Shibiao Jiang, et al. · 2021 - [Measuring Mathematical Problem Solving With the MATH Dataset](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-mathematical-problem-solving-math/) — Dan Hendrycks, Collin Burns, Saurav Kadavath, et al. · 2021 - [MiniF2F: a cross-system benchmark for formal Olympiad-level mathematics](https://md.research.elamur.ai/free/en/papers/zheng-2021-minif2f-cross-system-benchmark-formal/) — Kunhao Zheng, Jesse Michael Han, Stanislas Polu · 2021 - [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 - [TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance](https://md.research.elamur.ai/free/en/papers/zhu-2021-tat-qa-question-answering-benchmark/) — Fengbin Zhu, Wenqiang Lei, Youcheng Huang, et al. · 2021 - [The Lean 4 Theorem Prover and Programming Language (System Description)](https://md.research.elamur.ai/free/en/papers/moura-2021-lean-4-theorem-prover-programming/) — Leonardo de Moura, Sebastian Ullrich · 2021 - [Training Verifiers to Solve Math Word Problems](https://md.research.elamur.ai/free/en/papers/cobbe-2021-training-verifiers-solve-math-word/) — Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, et al. · 2021 - [Array Programming with NumPy](https://md.research.elamur.ai/free/en/papers/harris-2020-array-programming-numpy/) — Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, et al. · 2020 - [Generative Language Modeling for Automated Theorem Proving](https://md.research.elamur.ai/free/en/papers/polu-2020-generative-language-modeling-automated-theorem/) — Stanislas Polu, Ilya Sutskever · 2020 - [Injecting Numerical Reasoning Skills into Language Models](https://md.research.elamur.ai/free/en/papers/geva-2020-injecting-numerical-reasoning-skills-into/) — Mor Geva, Ankit Gupta, Jonathan Berant · 2020 - [Measuring Massive Multitask Language Understanding](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/) — Dan Hendrycks, Collin Burns, Steven Basart, et al. · 2020 - [Analysing Mathematical Reasoning Abilities of Neural Models](https://md.research.elamur.ai/free/en/papers/saxton-2019-analysing-mathematical-reasoning-abilities-neural/) — David Saxton, Edward Grefenstette, Felix Hill, et al. · 2019 - [Do NLP Models Know Numbers? Probing Numeracy in Embeddings](https://md.research.elamur.ai/free/en/papers/wallace-2019-do-nlp-models-know-numbers/) — Eric Wallace, Yizhong Wang, Sujian Li, et al. · 2019 - [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 - [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 [← prev](https://md.research.elamur.ai/free/en/shelves/mathematics/page/5/) [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](https://md.research.elamur.ai/free/en/shelves/mathematics/page/4/) [5](https://md.research.elamur.ai/free/en/shelves/mathematics/page/5/) **6** [7](https://md.research.elamur.ai/free/en/shelves/mathematics/page/7/) [next →](https://md.research.elamur.ai/free/en/shelves/mathematics/page/7/)