LM4OPT: Unveiling the Potential of Large Language Models in Formulating
  Mathematical Optimization Problems
Tasnim Ahmed Salimur Choudhury
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.