AI 中文总结
研究探讨大语言模型在专业翻译中找英语到法语对应词的作用，评估四个模型在两个领域的表现，比较两种提示策略，发现模型等存在差异，大语言模型对专业译者有用但不能取代语料库，为后续研究铺了路。
AI 中文摘要
专业翻译依赖文献和术语资源，包括语料库。这些资源对术语很有用，但编制和利用有局限，需时间、技术技能且数据难收集。本研究探讨大语言模型在帮助专业译者从英语到法语寻找对应词方面的作用。在地球、环境与行星科学及自然语言处理两个领域评估四个专有模型，基于每个领域80个术语，比较术语和翻译两种提示策略。结果显示模型、提示策略及领域间有明显差异，Claude Sonnet 4.5在最有利配置下效果最佳，DeepSeek稳定性高。信心估计分析表明其只是术语准确性的部分指标。总体而言，大语言模型对专业译者有用，但现阶段不能取代专业语料库，为未来研究其实际效用铺平道路。
英文摘要
Specialised translation relies on the use of documentary and terminological resources, including corpora. These resources are particularly useful for terminology. However, their compilation and exploitation have several limitations: they require time, technical skills and access to data that can be difficult to collect. This study examines the extent to which LLMs can assist specialised translators in finding equivalents from English to French. We evaluate four proprietary models, GPT-4o, GPT-5.2, Claude Sonnet 4.5 and DeepSeek, in two specialised domains, Earth, Environmental and Planetary Sciences (EEPS) and Natural Language Processing (NLP). The experiment is based on 80 terms per domain and compares two prompting strategies: a terminology and a translation mode. The results highlight clear differences between models, prompting strategies and, to a lesser extent, domains. Claude Sonnet 4.5 achieves the best results in the most favourable configuration, while DeepSeek stands out for its greater stability. Analysis of confidence estimates also shows that they are only a partial indicator of terminological accuracy. Overall, the findings suggest that LLMs can be useful tools for specialised translators, but cannot, at this stage, replace specialised corpora. This research therefore paves the way for future work on the real practical usefulness of LLMs for specialised translators in work and educational contexts.
Journal ref26th Annual Conference of the European Association for Machine Translation, Jun 2026, Tilburg, Netherlands
