Summary of Llmopt: Learning to Define and Solve General Optimization Problems From Scratch, by Caigao Jiang et al.
LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch
by Caigao Jiang, Xiang Shu, Hong Qian, Xingyu Lu, Jun Zhou, Aimin Zhou, Yang Yu
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 A unified learning-based framework called LLMOPT is proposed to boost optimization generalization, leveraging large language models (LLMs) to automate problem formulation and solving. The LLMOPT framework constructs a universal model from natural language descriptions of optimization problems and a pre-trained LLM, enabling the definition of diverse optimization problem types. Multi-instruction tuning enhances both problem formalization and solver code generation accuracy and generality. To prevent hallucinations in LLMs, the model alignment and self-correction mechanism are adopted. Extensive experiments across six real-world datasets covering roughly 20 fields show that LLMOPT achieves a notable 11.08% average solving accuracy improvement compared with state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to use large language models (LLMs) to help solve optimization problems. Optimization problems are important in many areas, such as health and energy. The problem is that current methods for using LLMs to solve these problems aren’t very good at handling different types of optimization problems. This paper introduces LLMOPT, a new framework that can learn to define many different types of optimization problems from natural language descriptions. The framework uses multi-instruction tuning to improve its accuracy and generality. To make sure the results are reliable, the authors also added a mechanism to prevent LLMs from making mistakes. They tested their method on six real-world datasets covering 20 fields and found that it can solve many types of optimization problems with an average improvement of 11.08% compared to current methods. |
Keywords
» Artificial intelligence » Alignment » Generalization » Instruction tuning » Optimization
