The New Quality of Translation Productivity and the Transformation of Teaching Paradigms Based on Large Language Models
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Abstract
Intelligent semantic information processing and adaptive knowledge generation have become key enabling technologies for next-generation communication and information systems. This study proposes a New Quality of Translation Productivity framework based on Large Language Models (NQTP-LLM) for intelligent multilingual information processing and adaptive educational support. The framework integrates transformer-based neural machine translation, Direct Preference Optimization (DPO), Retrieval-Augmented Generation (RAG), semantic embedding representation, and human-in-the-loop optimization to enhance contextual consistency, semantic fidelity, and translation efficiency. A multimodal translation evaluation architecture is established using semantic feature extraction, contextual knowledge retrieval, quality assessment, and adaptive feedback mechanisms. Experiments conducted on the AI vs TTM Translation Evaluation Dataset demonstrate that the proposed framework achieves a BLEU score of 0.961, with substantial improvements in METEOR, ROUGE, chrF, BERTScore, COMET, BLEURT, Google-BLEU, and NIST metrics while reducing post-editing effort by 3.9%. The results verify the effectiveness of integrating intelligent knowledge retrieval, semantic information fusion, and adaptive optimization for high-accuracy multilingual information processing. The proposed framework provides a practical approach for intelligent communication systems, semantic information services, human–AI collaborative decision support, and next-generation knowledge-centric digital environments.
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