LLMs Show Promise for Specialized Translation but Can't Replace Corpora

A new study published on arXiv evaluates the effectiveness of Large Language Models (LLMs) in assisting specialized translators with terminology translation from English to French. The research tested four models—GPT-4o, GPT-5.2, Claude Sonnet 4.5, and DeepSeek—across the domains of Earth, Environmental and Planetary Sciences (EEPS) and Natural Language Processing (NLP). Results indicated significant differences in model performance and prompting strategies, with Claude Sonnet 4.5 performing best in optimal conditions and DeepSeek showing more consistent results. While LLMs can be valuable tools, the study concludes they cannot yet fully replace specialized corpora for translation tasks. AI

IMPACT LLMs show potential as assistive tools for specialized translators, though they do not yet replace traditional corpora.

RANK_REASON The cluster contains an academic paper published on arXiv detailing research findings on LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Claude Sonnet 4.5
• DeepSeek
• Earth, Environmental and Planetary Sciences
• GPT-4o
• GPT-5.2
• Hugging Face
• Joachim Minder
• Natural Language Processing

• paper
• other

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