Summary of Orlm: a Customizable Framework in Training Large Models For Automated Optimization Modeling, by Chenyu Huang et al.
ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling
by Chenyu Huang, Zhengyang Tang, Shixi Hu, Ruoqing Jiang, Xin Zheng, Dongdong Ge, Benyou Wang, Zizhuo Wang
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed paper tackles the challenge of automating optimization modeling using large language models (LLMs). Existing approaches rely on closed-source LLMs, which requires extensive prompt engineering techniques and is hindered by the scarcity of high-quality training datasets. The researchers introduce OR-Instruct, a semi-automated data synthesis framework for optimization modeling, and IndustryOR, an industrial benchmark for evaluating LLMs in solving practical operations research (OR) problems. They train open-source LLMs using synthesized data, achieving state-of-the-art performance across various benchmarks. The study also explores the potential of scaling law and reinforcement learning to further enhance the performance of these models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at solving real-world problems. It uses special computer programs called large language models (LLMs) to help with this task. Right now, people have to do a lot of work to make these LLMs work well, and it’s hard to get the right data to train them. The researchers created new tools that can make training easier and more efficient. They also tested their ideas on some big problems and found that they worked really well. This could be very useful for businesses and industries who need help solving complex problems. |
Keywords
» Artificial intelligence » Optimization » Prompt » Reinforcement learning
