LM4OPT: Unveiling the Potential of Large Language Models in Formulating Mathematical Optimization Problems

• URL: http://arxiv.org/abs/2403.01342v1
• Date: Sat, 2 Mar 2024 23:32:33 GMT
• Title: LM4OPT: Unveiling the Potential of Large Language Models in Formulating Mathematical Optimization Problems
• Authors: Tasnim Ahmed, Salimur Choudhury
• Abstract summary: This study compares prominent Large Language Models, including GPT-3.5, GPT-4, and Llama-2-7b, in zero-shot and one-shot settings. Our findings show GPT-4's superior performance, particularly in the one-shot scenario.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the rapidly evolving field of natural language processing, the translation of linguistic descriptions into mathematical formulation of optimization problems presents a formidable challenge, demanding intricate understanding and processing capabilities from Large Language Models (LLMs). This study compares prominent LLMs, including GPT-3.5, GPT-4, and Llama-2-7b, in zero-shot and one-shot settings for this task. Our findings show GPT-4's superior performance, particularly in the one-shot scenario. A central part of this research is the introduction of `LM4OPT,' a progressive fine-tuning framework for Llama-2-7b that utilizes noisy embeddings and specialized datasets. However, this research highlights a notable gap in the contextual understanding capabilities of smaller models such as Llama-2-7b compared to larger counterparts, especially in processing lengthy and complex input contexts. Our empirical investigation, utilizing the NL4Opt dataset, unveils that GPT-4 surpasses the baseline performance established by previous research, achieving an F1-score of 0.63, solely based on the problem description in natural language, and without relying on any additional named entity information. GPT-3.5 follows closely, both outperforming the fine-tuned Llama-2-7b. These findings not only benchmark the current capabilities of LLMs in a novel application area but also lay the groundwork for future improvements in mathematical formulation of optimization problems from natural language input.

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