Researchers have developed a retrieval-augmented generation (RAG) system combined with prompting techniques to improve Japanese-Chinese machine translation, particularly for sentences with noun-modifying clause constructions (NMCCs). The system integrates linguistic analysis, embedding-based retrieval, and prompt engineering to enhance the output of large language models like GPT-4o. Testing with various knowledge base sizes showed a significant increase in BLEU scores, with larger bases yielding better results, demonstrating an interpretable and auditable method for translation improvement. AI
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IMPACT This RAG+prompt system offers a more interpretable and auditable approach to improving LLM translation quality for complex linguistic structures.
RANK_REASON The cluster contains an academic paper detailing a new RAG+prompt system for machine translation.