no code implementations • 26 Feb 2024 • Zimu Lu, Aojun Zhou, Houxing Ren, Ke Wang, Weikang Shi, Junting Pan, Mingjie Zhan, Hongsheng Li
We augment the ground-truth solutions of our seed data and train a back-translation model to translate the augmented solutions back into new questions.
no code implementations • 22 Feb 2024 • Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Mingjie Zhan, Hongsheng Li
Recent advancements in Large Multimodal Models (LMMs) have shown promising results in mathematical reasoning within visual contexts, with models approaching human-level performance on existing benchmarks such as MathVista.
Ranked #1 on Multimodal Reasoning on MATH-V (using extra training data)
1 code implementation • 5 Oct 2023 • Ke Wang, Houxing Ren, Aojun Zhou, Zimu Lu, Sichun Luo, Weikang Shi, Renrui Zhang, Linqi Song, Mingjie Zhan, Hongsheng Li
In this paper, we present a method to fine-tune open-source language models, enabling them to use code for modeling and deriving math equations and, consequently, enhancing their mathematical reasoning abilities.
Ranked #4 on Math Word Problem Solving on SVAMP (using extra training data)
1 code implementation • 15 Aug 2023 • Aojun Zhou, Ke Wang, Zimu Lu, Weikang Shi, Sichun Luo, Zipeng Qin, Shaoqing Lu, Anya Jia, Linqi Song, Mingjie Zhan, Hongsheng Li
We found that its success can be largely attributed to its powerful skills in generating and executing code, evaluating the output of code execution, and rectifying its solution when receiving unreasonable outputs.
Ranked #2 on Math Word Problem Solving on MATH
1 code implementation • CVPR 2023 • Wenliang Zhao, Yongming Rao, Weikang Shi, Zuyan Liu, Jie zhou, Jiwen Lu
Unlike previous work that relies on carefully designed network architectures and loss functions to fuse the information from the source and target faces, we reformulate the face swapping as a conditional inpainting task, performed by a powerful diffusion model guided by the desired face attributes (e. g., identity and landmarks).