Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model

Yu-Chen Lin; Akhilesh Kumar; Norman Chang; Wenliang Zhang; Muhammad Zakir; Rucha Apte; Haiyang He; Chao Wang; Jyh-Shing Roger Jang

arXiv · 2311.16267

Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model

Yu-Chen Lin·Akhilesh Kumar·Norman Chang·Wenliang Zhang·Muhammad Zakir·Rucha Apte·Haiyang He·Chao Wang·Jyh-Shing Roger Jang

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Abstract

We present four main contributions to enhance the performance of Large Language Models (LLMs) in generating domain-specific code: (i) utilizing LLM-based data splitting and data renovation techniques to improve the semantic representation of embeddings' space; (ii) introducing the Chain of Density for Renovation Credibility (CoDRC), driven by LLMs, and the Adaptive Text Renovation (ATR) algorithm for assessing data renovation reliability; (iii) developing the Implicit Knowledge Expansion and Contemplation (IKEC) Prompt technique; and (iv) effectively refactoring existing scripts to generate new and high-quality scripts with LLMs. By using engineering simulation software RedHawk-SC as a case study, we demonstrate the effectiveness of our data pre-processing method for expanding and categorizing scripts. When combined with IKEC, these techniques enhance the Retrieval-Augmented Generation (RAG) method in retrieving more relevant information, ultimately achieving a 73.33% "Percentage of Correct Lines" for code generation problems in MapReduce applications.

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Yu-Chen Lin, Akhilesh Kumar, Norman Chang, Wenliang Zhang, Muhammad Zakir, Rucha Apte, Haiyang He, Chao Wang, Jyh-Shing Roger Jang. 2023-11-27. Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model. https://arxiv.org/abs/2311.16267

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