Основные понятия
The author explores the performance of various Large Language Models (LLMs) in formulating optimization problems from natural language descriptions, highlighting GPT-4's superior performance and the limitations of smaller models like Llama-2-7b. The research introduces a progressive fine-tuning framework, LM4OPT, to enhance Llama-2-7b's specificity for this task.
Аннотация
The content delves into comparing different Large Language Models (LLMs) such as GPT-3.5, GPT-4, and Llama-2-7b in formulating optimization problems from natural language descriptions. It emphasizes GPT-4's exceptional performance and introduces a novel fine-tuning approach for Llama-2-7b using the LM4OPT framework. The study also discusses the challenges faced by smaller models in handling complex contexts and provides insights into improving model performance for intricate tasks.
Статистика
(500.0) * cleaners + (350.0) * receptionists ≤ 30000.0
F1-score of 0.63 achieved by GPT-4
GSM8K dataset used for fine-tuning Llama-2-7b
23.52 grams of CO2 emissions per fine-tuning session
Цитаты
"Progressive fine-tuning combined with NEFTune significantly enhances the ability to understand and solve optimization problems."
"GPT-4 exhibits superior performance in both zero-shot and one-shot scenarios."