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

• URL: http://arxiv.org/abs/2311.16267v2
• Date: Tue, 30 Jan 2024 08:00:07 GMT
• Title: Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model
• Authors: Yu-Chen Lin, Akhilesh Kumar, Norman Chang, Wenliang Zhang, Muhammad Zakir, Rucha Apte, Haiyang He, Chao Wang, Jyh-Shing Roger Jang
• Abstract summary: We present four main contributions to enhance the performance of Large Language Models (LLMs) in generating domain-specific code. We introduce the Chain of Density for Renovation Credibility (CoDRC), driven by LLMs, and the Adaptive Text Renovation (ATR) algorithm for assessing data reliability. We also develop the Implicit Knowledge Expansion and Contemplation (IKEC) Prompt technique.
• Score: 7.74830226656449
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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