Summary of Optimus-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale, by Ali Ahmaditeshnizi et al.
OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale
by Ali AhmadiTeshnizi, Wenzhi Gao, Herman Brunborg, Shayan Talaei, Connor Lawless, Madeleine Udell
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 research introduces OptiMUS-0.3, a Large Language Model (LLM)-based system designed to formulate and solve mixed integer linear programming problems from their natural language descriptions. The system is capable of developing mathematical models, writing and debugging solver code, evaluating the generated solutions, and improving efficiency and correctness based on these evaluations. OptiMUS-0.3 utilizes a modular structure to process problems with long descriptions and complex data without requiring lengthy prompts. Experimental results demonstrate that it outperforms existing state-of-the-art methods on easy datasets by more than 12% and hard datasets (including the newly released NLP4LP dataset) by over 8%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about creating a computer system that can understand and solve complicated math problems from written descriptions. The system, called OptiMUS-0.3, can read and make sense of long and complex problem statements, then use this information to create mathematical models and find the best solutions. What’s impressive is that it’s able to do this better than existing methods, solving problems faster and more accurately. |
Keywords
» Artificial intelligence » Large language model
