Summary of Leveraging Large Language Models For Solving Rare Mip Challenges, by Teng Wang et al.
Leveraging Large Language Models for Solving Rare MIP Challenges
by Teng Wang, Wing-Yin Yu, Ruifeng She, Wenhan Yang, Taijie Chen, Jianping Zhang
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Optimization and Control (math.OC)
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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 Mixed Integer Programming (MIP) has been extensively applied to solve complex problems within tight time constraints. However, as problem scale increases, MIP model formulation and finding feasible solutions become more challenging. In contrast, large language models (LLMs), such as GPT-4, can handle traditional medium-scale MIP problems without fine-tuning. Despite this, they struggle with uncommon or highly specialized MIP scenarios. Fine-tuning LLMs can yield feasible solutions for medium-scale MIP instances, but these models typically fail to explore diverse solutions when constrained by a low and constant temperature. To address this limitation, we propose a recursively dynamic temperature method integrated with a chain-of-thought approach. Our findings show that starting with a high temperature and gradually lowering it leads to better feasible solutions compared to other dynamic temperature strategies. Additionally, our results demonstrate that LLMs can produce solutions that complement traditional solvers by accelerating the pruning process and improving overall efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to solve complex math problems called Mixed Integer Programming (MIP). MIP helps us find answers quickly, but it gets harder when we need to solve really big problems. Large language models (LLMs) are good at solving some medium-sized MIP problems without needing extra help. However, they struggle with very unusual or specialized MIP problems. To make LLMs better at solving these problems, we came up with a new way to use temperature to help them find more answers. Our research shows that starting with high temperatures and slowly lowering them helps LLMs find better solutions. We also compared our results with those from another computer program called Gurobi, and found that LLMs can help speed up the solving process and make it more efficient. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Pruning » Temperature
