Summary of Geoeval: Benchmark For Evaluating Llms and Multi-modal Models on Geometry Problem-solving, by Jiaxin Zhang et al.
GeoEval: Benchmark for Evaluating LLMs and Multi-Modal Models on Geometry Problem-Solving
by Jiaxin Zhang, Zhongzhi Li, Mingliang Zhang, Fei Yin, Chenglin Liu, Yashar Moshfeghi
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The recent advancements in large language models (LLMs) and multi-modal models (MMs) have showcased their impressive problem-solving abilities. However, their proficiency in tackling geometry math problems, which requires an integrated understanding of textual and visual information, has not been thoroughly evaluated. To address this gap, the authors introduce the GeoEval benchmark, a comprehensive collection that includes various subsets for evaluating LLMs’ and MMs’ performance in solving geometry math problems. The evaluation reveals that the WizardMath model excels, but with significant drops in accuracy when faced with harder problems. This highlights the need for testing models against datasets on which they have not been pre-trained. Additionally, GPT-series models perform better when rephrasing problems, suggesting a promising method for enhancing model capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models and multi-modal models are super smart and can solve many problems! But, they don’t do very well with math problems that involve shapes and pictures. This is because these models aren’t good at understanding both words and images together. To help fix this problem, researchers created a special test called GeoEval. It has lots of different math problems for the models to try and solve. When they tested the models, they found out that one model named WizardMath did really well! But it didn’t do so great when the problems got harder. This shows us that these models need to be tested in new ways to see how well they can really do. |
Keywords
» Artificial intelligence » Gpt » Multi modal
