Math-PUMA: Progressive Upward Multimodal Alignment to Enhance Mathematical Reasoning

Analysing mathematical reasoning abilities of neural models

Original

Saxton, D.; Grefenstette, E.; Hill, F.; and Kohli, P. 2019 · 1904

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Adam: A method for stochastic optimization

Original

Kingma, D. P.; and Ba, J. 2014 · 2014

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Learning Transferable Visual Models from Natural Language Supervision

Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021

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Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks

Original

Chen, W.; Ma, X.; Wang, X.; and Cohen, W. W. 2022 · 2022

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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Li, J.; Li, D.; Xiong, C.; and Hoi, S. 2022 · 2022

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Training language models to follow instructions with human feedback

Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Gray, A.; Schulman, J.; Hilton, J.; Kelton, F.; Miller, L.; Simens, M.; Askell, A.; Welinder, P.; Christiano, P.; Leike, J.; and Lowe, R. 2022 · 2022

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

Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Xia, F.; Chi, E.; Le, Q. V.; Zhou, D.; et al. 2022 · 2022

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Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities

Original

Bai, J.; Bai, S.; Yang, S.; Wang, S.; Tan, S.; Wang, P.; Lin, J.; Zhou, C.; and Zhou, J. 2023 · 2023

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Gemini: a family of highly capable multimodal models

Original

Gemini Team, G. 2023 · 2023

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Tora: A tool-integrated reasoning agent for mathematical problem solving

Original

Gou, Z.; Shao, Z.; Gong, Y.; Yang, Y.; Huang, M.; Duan, N.; Chen, W.; et al. 2023 · 2023

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Visual Instruction Tuning

Liu, H.; Li, C.; Wu, Q.; and Lee, Y. J. 2023 · 2023

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Metamath: Bootstrap your own mathematical questions for large language models

Original

Yu, L.; Jiang, W.; Shi, H.; Yu, J.; Liu, Z.; Zhang, Y.; Kwok, J. T.; Li, Z.; Weller, A.; and Liu, W. 2023 · 2023

Cited alongside, same era.

Mammoth: Building math generalist models through hybrid instruction tuning

Original

Yue, X.; Qu, X.; Zhang, G.; Fu, Y.; Huang, W.; Sun, H.; Su, Y.; and Chen, W. 2023 · 2023

Cited alongside, same era.

Sigmoid loss for language image pre-training

Zhai, X.; Mustafa, B.; Kolesnikov, A.; and Beyer, L. 2023 · 2023

Cited alongside, same era.

GeoGPT4V: Towards Geometric Multi-modal Large Language Models with Geometric Image Generation

Original

Cai, S.; Bao, K.; Guo, H.; Zhang, J.; Song, J.; and Zheng, B. 2024 · 2024

Cited alongside, same era.

How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?

Original

Qiao, R.; Tan, Q.; Dong, G.; Wu, M.; Sun, C.; Song, X.; GongQue, Z.; Lei, S.; Wei, Z.; Zhang, M.; et al. 2024 · 2024

Closest in time.

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Original

Reid, M.; Savinov, N.; Teplyashin, D.; Lepikhin, D.; Lillicrap, T.; Alayrac, J.-b.; Soricut, R.; Lazaridou, A.; Firat, O.; Schrittwieser, J.; et al. 2024 · 2024

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DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Original

Shao, Z.; Wang, P.; Zhu, Q.; Xu, R.; Song, J.; Zhang, M.; Li, Y.; Wu, Y.; and Guo, D. 2024 · 2024

Closest in time.

Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models

Original

Shi, W.; Hu, Z.; Bin, Y.; Liu, J.; Yang, Y.; Ng, S.-K.; Bing, L.; and Lee, R. K.-W. 2024 · 2024

Closest in time.