Description: Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset
mathvision (3) math vision (2)
Large language models (LLMs) have exhibited great potential in mathematical reasoning. However, there remains a performance gap in this area between existing open-source models and closed-source models such as GPT-4. In this paper, we introduce MathGenie , a novel method for generating diverse and reliable math problems from a small-scale problem-solution dataset (denoted as seed data ). We augment the ground-truth solutions of our seed data and train a back-translation model to translate the augmented solu
Framework of MathGenie. Iterative Solution Augmentation augments human-annotated solutions in GSM8K and MATH to create new solutions, as shown in Step 1. These solutions are then back-translated to new questions using Question Back-translation, demonstrated in Step 2. Then reliable code-integrated solutions are curated using Verification-Based Solution Filtering, by generating solutions and filtering them using verification rationales, as shown in Step 3.
Iterative Solution Augmentation and Question Back-translation aims to generate diverse and reliable math problems. The proposed math problem back-translation leverages the constraints and logical relationships inherent in mathematical solutions to create a diverse and high-quality set of new math problems. Specifically, we iteratively augment the human-annotated solutions from the relatively small training sets of MATH and GSM8K, generating a large-scale collection of augmented new solutions. These solution