Low-Cost Language Models: Survey and Performance Evaluation on Python Code Generation

Discover the first version of our scientific publication "Low-cost deep language models: Survey and performance evaluation on Python code generation" published in arxiv and submitted to the Engineering Applications of Artificial Intelligence journal. This article is already available to the public.

Thanks to the Novelis research team - including Jessica López Espejel, Mahaman Sanoussi Yahaya Alassan, Merieme Bouhandi, Walid Dahhane, El Hassane Ettifouri - for their know-how and expertise.

Abstract

"Large Language Models (LLMs) have become the go-to solution for many Natural Language Processing (NLP) tasks due to their ability to tackle various problems and produce high-quality results. Specifically, they are increasingly used to automatically generate code, easing the burden on developers by handling repetitive tasks. However, this improvement in quality has led to high computational and memory demands, making LLMs inaccessible to users with limited resources. In this paper, we focus on Central Processing Unit (CPU)-compatible models and conduct a thorough semi-manual evaluation of their strengths and weaknesses in generating Python code. We enhance their performance by introducing a Chain-of-Thought prompt that guides the model in problem-solving. Additionally, we propose a dataset of 60 programming problems with varying difficulty levels for evaluation purposes. Our assessment also includes testing these models on two state-of-the-art datasets: HumanEval and EvalPlus. We commit to sharing our dataset and experimental results publicly to ensure transparency."

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