GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models

• URL: http://arxiv.org/abs/2410.13510v1
• Date: Thu, 17 Oct 2024 12:56:52 GMT
• Title: GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models
• Authors: Aditya Sharma, Aman Dalmia, Mehran Kazemi, Amal Zouaq, Christopher J. Pal,
• Abstract summary: Vision-parametric models (VLMs) have made significant progress in various multimodal tasks. They still struggle with geometry problems and are significantly limited by their inability to perform mathematical operations not seen during pre-training. We present GeoCoder, which leverages modular code-finetuning to generate and execute code using a predefined geometry function library.
• Score: 10.443672399225983
• License:
• Abstract: Geometry problem-solving demands advanced reasoning abilities to process multimodal inputs and employ mathematical knowledge effectively. Vision-language models (VLMs) have made significant progress in various multimodal tasks. Yet, they still struggle with geometry problems and are significantly limited by their inability to perform mathematical operations not seen during pre-training, such as calculating the cosine of an arbitrary angle, and by difficulties in correctly applying relevant geometry formulas. To overcome these challenges, we present GeoCoder, which leverages modular code-finetuning to generate and execute code using a predefined geometry function library. By executing the code, we achieve accurate and deterministic calculations, contrasting the stochastic nature of autoregressive token prediction, while the function library minimizes errors in formula usage. We also propose a multimodal retrieval-augmented variant of GeoCoder, named RAG-GeoCoder, which incorporates a non-parametric memory module for retrieving functions from the geometry library, thereby reducing reliance on parametric memory. Our modular code-finetuning approach enhances the geometric reasoning capabilities of VLMs, yielding an average improvement of over 16% across various question complexities on the GeomVerse dataset compared to other finetuning methods.

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