Research on the Application of Hybrid Linformers and LLM Models for Efficient Chinese-English Tourism Translation Process

The explosive growth of global tourism has triggered the need for an intelligent translation platform for exploring the cultural heritage of traditional places, especially needed for Chinese-English. The recent advancements in the deep learning algorithms has shown the bright light of performance in the machine translation process. However, computational complexity and resource requirements remains to be bottleneck for the real-time implementation process. Additionally, the existing frameworks also suffer from the low translation accuracy due to the non-availability of large data corpuses. To overcome this challenge, this research article proposes a Hybrid Linformer-MISTRAL 3B framework designed for efficient Chinese-English tourism translation. The model integrates Linear attention mechanisms with low-rank approximations in MISTRAL 7B to reduce redundant computations while preserving semantic fidelity. Furthermore, domain-specific fine-tuning on tourism-oriented bilingual corpora enhances contextual accuracy, cultural adaptability, and idiomatic translation quality. Experimental evaluation on OPUS which demonstrates the proposed framework achieves competitive BLEU score of 0.89, ChrF, of 0.82 and WER is less than 0.01 and scores compared to dense transformer baselines, while significantly reducing inference latency and memory footprint. The results indicate that hybrid sparse-transformer LLM architectures are a promising direction for developing scalable intelligent framework for achieving an efficient Chinese-English Translation