Research on Natural Language Instruction Recognition Method Based on Large Language Models

In the context of the swift and significant advancement of large language models, the user demographic and the array of application scenarios for artificial intelligence have experienced a remarkable and extensive expansion. The capability to accurately recognize and interpret linguistic instructions has emerged as the fundamental cornerstone upon which AI systems rely to comprehend and effectively execute a diverse range of tasks. The ongoing enhancement of natural language instruction recognition technology holds paramount importance for substantially improving the efficiency and precision with which language models can handle intricate, information-intensive tasks. This scholarly paper delves deeply into the current state of research, focusing on critical aspects such as the extraction of sensitive information, advancements in model cognitive capabilities, and the strategies employed to mitigate the impact of various interference factors. It conducts a comprehensive and systematic analysis, juxtaposing different methodologies, including the hybrid approach that integrates Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for the precise extraction of sensitive information, the innovative open-world multi-scenario instruction understanding (framework known as MOO, and the sophisticated semantic enhancement technique of retrieval-augmented generation (RAG). The paper meticulously examines the processing workflows inherent to these methodologies, scrutinizing their respective influences on the analysis of linguistic instructions. Furthermore, it synthesizes and highlights the unique advantages that each method brings to the table, providing a nuanced understanding of their contributions to the field.