Hybrid Architectures for Chinese Text Processing: Optimizing LLaMA2 with CNN and LSTM

In the rapidly evolving field of natural language processing (NLP), the processing of the Chinese language, with its unique complexities, presents significant challenges, especially in the context of Large Language Models (LLMs) like LLaMA2. These challenges are further exacerbated by the presence of non-standardized text prevalent across digital Chinese content. To address these challenges, this paper proposes a novel hybrid approach that seamlessly integrates deep contextual embeddings with Convolutional Neural Networks (CNNs) to enhance the processing of standardized Chinese text. Our approach involves a multi-stage process wherein deep contextual embeddings are first utilized to capture the nuanced semantic relationships within text. Following this, CNNs are employed to identify and exploit structural and syntactic patterns, facilitating a comprehensive understanding of the text. This hybrid model significantly improves LLaMA2’s efficiency and accuracy across various Chinese text processing tasks by ensuring that both semantic depth and structural nuances are accurately captured. The effectiveness of our model is demonstrated through rigorous testing across several benchmarks, showcasing its superiority in processing Chinese text with enhanced accuracy and speed. This research not only contributes to the advancement of text processing capabilities of LLMs but also opens new avenues for their application in tasks such as automated translation and sentiment analysis.