Data Redundancy Elimination and Noise Processing via Large Language Model Prompt Engineering

With the rapid advancement of artificial intelligence (AI), intelligent systems have been widely applied across various practical domains. During their experimental processes, data collection often occurs in complex environments, leading to challenges in ensuring data quality and integrity. Two significant issues are data redundancy and data noise, which can degrade model performance and impact the generalization ability and prediction accuracy of AI models. Traditional methods for handling these issues are either rule-based, relying heavily on domain knowledge, or machine learning-based, which require large volumes of high-quality training data and significant computational resources. In this paper, we propose a novel approach leveraging Large Language Models (LLMs) combined with the Sequential Chain optimization algorithm to address data redundancy elimination and noise processing. By designing automated prompt templates tailored to experimental data characteristics and utilizing LLMs' extensive knowledge and reasoning capabilities, our approach improves the effectiveness of preprocessing tasks. Additionally, the Sequential Chain technique enhances LLM processing per-formance, reducing the hallucination phenomenon and ensuring higher accuracy in data preprocessing. Our experimental results demonstrate that LLM-based methods can achieve results comparable to traditional techniques while offering greater scalability and adaptability. Future work could focus on developing more efficient LLMs with lower computational requirements and refining prompt engineering techniques to reduce the time investment needed, making these advanced methods more accessible and practical for a broader range of applications.