MathFusion: Enhancing Mathematical Problem-solving of LLM through Instruction Fusion

Augmenting data with mixup for sentence classification: An empirical study

Original

Hongyu Guo, Yongyi Mao, and Richong Zhang. 2019 · 1905

Earlier work this paper cites.

Visualizing data using t-sne

Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008

Earlier work this paper cites.

mixup: Beyond empirical risk minimization

Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz. 2018 · 2018

Earlier work this paper cites.

Problem types and open innovation governance modes: A project-level empirical exploration

Mehdi Bagherzadeh, Andrei Gurca, and Sabine Brunswicker. 2019 · 2019

Earlier work this paper cites.

Analysing mathematical reasoning abilities of neural models

David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli. 2019 · 2019

Earlier work this paper cites.

On mixup training: Improved calibration and predictive uncertainty for deep neural networks

Sunil Thulasidasan, Gopinath Chennupati, Jeff A. Bilmes, Tanmoy Bhattacharya, and Sarah Michalak. 2019 · 2019

Earlier work this paper cites.

Sequence-level mixed sample data augmentation

Demi Guo, Yoon Kim, and Alexander M. Rush. 2020 · 2020

Earlier work this paper cites.

Seqmix: Augmenting active sequence labeling via sequence mixup

Rongzhi Zhang, Yue Yu, and Chao Zhang. 2020 · 2020

Earlier work this paper cites.

Training verifiers to solve math word problems

Original

Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021 · 2021

Earlier work this paper cites.

Measuring mathematical problem solving with the math dataset

Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021 · 2021

Earlier work this paper cites.

Analysis of mathematical concept understanding capabilities: The impact of makerspae stem learning approach models and student learning activities

Komarudin Komarudin, Suherman Suherman, and Anita Anggraini. 2021 · 2021

Earlier work this paper cites.

mixseq: A simple data augmentation methodfor neural machine translation

Xueqing Wu, Yingce Xia, Jinhua Zhu, Lijun Wu, Shufang Xie, Yang Fan, and Tao Qin. 2021 · 2021

Earlier work this paper cites.

Chain-of-thought prompting elicits reasoning in large language models

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022 · 2022

Earlier work this paper cites.

TheoremQA: A theorem-driven question answering dataset

Wenhu Chen, Ming Yin, Max Ku, Pan Lu, Yixin Wan, Xueguang Ma, Jianyu Xu, Xinyi Wang, and Tony Xia. 2023 · 2023

Earlier work this paper cites.

Towards reasoning in large language models: A survey

Jie Huang and Kevin Chen-Chuan Chang. 2023 · 2023

Earlier work this paper cites.

Mistral 7b

Original

Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023

Earlier work this paper cites.

Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct

Original

Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, Jianguang Lou, Chongyang Tao, Xiubo Geng, Qingwei Lin, Shifeng Chen, and Dongmei Zhang. 2023 · 2023

Cited alongside, same era.

Gpt-4 technical report

Original

Josh OpenAI, Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023

Cited alongside, same era.

Problem decomposition skills, mathematical maturity, and their relation to mathematics problem-solving in a computer science learning class

Harsa Wara Prabawa, Rizky Rosjanuardi, and Elah Nurlaelah. 2023 · 2023

Cited alongside, same era.

Scaling relationship on learning mathematical reasoning with large language models

Original

Zheng Yuan, Hongyi Yuan, Chengpeng Li, Guanting Dong, Chuanqi Tan, and Chang Zhou. 2023 · 2023

Cited alongside, same era.

Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight-fold

Original

Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, and Aviral Kumar. 2024 · 2024

Later among the works it cites.

Deepseekmath: Pushing the limits of mathematical reasoning in open language models

Original

Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. 2024 · 2024

Later among the works it cites.

What makes math word problems challenging for llms?

KV Aditya Srivatsa and Ekaterina Kochmar. 2024 · 2024

Later among the works it cites.

Mathscale: Scaling instruction tuning for mathematical reasoning

Zhengyang Tang, Xingxing Zhang, Benyou Wang, and Furu Wei. 2024 · 2024

Later among the works it cites.

Learning to reason with llms

Mistral AI team. 2024 · 2024

Later among the works it cites.