Summary of Llms For Mathematical Modeling: Towards Bridging the Gap Between Natural and Mathematical Languages, by Xuhan Huang et al.
LLMs for Mathematical Modeling: Towards Bridging the Gap between Natural and Mathematical Languages
by Xuhan Huang, Qingning Shen, Yan Hu, Anningzhe Gao, Benyou Wang
First submitted to arxiv on: 21 May 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 This paper addresses the challenge of evaluating Large Language Models’ (LLMs) ability to perform mathematical reasoning. Despite their strong natural language processing capabilities, LLMs struggle with complex mathematical tasks. To address this gap, the authors propose a process-oriented framework for evaluating LLMs’ modeling abilities using solvers to compare outputs with ground truth. The proposed benchmark, Mamo, comprises 1,209 questions covering ordinary differential equations, linear programming, and mixed-integer linear programming. Experimental results show that existing LLMs struggle with complex mathematical modeling tasks, with larger models demonstrating superior performance. This work is a crucial step towards developing Artificial General Intelligence (AGI) capabilities in LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper tries to figure out how well computers can do math problems using special language models. These computer programs are really good at understanding and generating human language, but they’re not as good at doing math problems. The researchers want to make it easier to test these computer programs’ math skills, so they came up with a new way to do it. They created a big list of math questions that the computer programs can try to answer, and then compared their answers to the correct answers. This helps us understand how well these computer programs can do math problems, and where we need to improve them. |
Keywords
» Artificial intelligence » Natural language processing
