梦瑶 发自 凹非寺 量子位 | 公众号 QbitAI "开组会是一场巨大的精神霸凌。" 哪怕毕业了这么多年，在社交软件看到这几个字的时候心里还是咯噔了一下… 毕竟，导师一句"这些文献你先读一读哈～"往往意味着：马上要啃几十页外文PDF、上百条术语、无数个长难句！！！！（难啊……） 就在我们"苦中作业"的时候，我发现了百度翻译里的一个隐藏板块——叫「 文档翻译 」，据说不仅翻译水平在线，支持 200+ 种语言翻译， 连文章的排版、格式都能高度还原？ 这么好的功能当然不放过，我立刻把它拉进横评跑了一遍，测下来最直观的感受是—— 翻译够准、排版能打，连对照阅读都安排上了： 不仅如此，翻的时候旁边还挂着个AI助手，能一边总结文章内容、一边帮你划重点，嗯..感觉像是自带了个贴心助教？ 这次我特意把百度「文档翻译」、Google、DeepL拉出来做了个正面对比，苦啃文献的学生党、牛马党有救了？？ 话不多说，直接看实测！ 翻译「基本功」大比拼 讲真的，我们在阅读文献、查阅资料的时候，打开翻译工具从来不是为了单纯做个"英译中"，我们真正看中的，是那些能替我们「省力」的能 力。 最好还能一边翻译一边总结重点，毕竟一篇论文动辄上 ...

Core Viewpoint - The article discusses the launch of TransBench, the first application-based AI translation evaluation ranking system, aimed at standardizing translation quality across various AI models [1][5][32]. Group 1: TransBench Overview - TransBench is a collaborative effort by Alibaba International AI Business, Shanghai Artificial Intelligence Laboratory, and Beijing Language University [2]. - It introduces new evaluation metrics such as hallucination rate, cultural taboo words, and politeness norms, addressing common issues in large model translations [3][34]. - The evaluation system is open-source and has released its first set of results, inviting AI translation institutions to participate [5][6][44]. Group 2: Evaluation Metrics - The evaluation framework categorizes data sets into three main types: general standards, e-commerce culture, and cultural characteristics [8][35]. - The ranking assesses translation capabilities based on four dimensions: overall score, general standards, e-commerce culture, and cultural characteristics [9][11]. Group 3: Model Performance - In the English-to-other-languages category, the top three models based on overall score and general standards are GPT-4o, DeepL Translate, and GPT-4-Turbo [16][14]. - For the e-commerce sector, DeepSeek-R1 ranks among the top performers, with Qwen2.5 models excelling in cultural characteristics [17][19]. - In the Chinese-to-other-languages category, DeepSeek-V3 leads, followed by Gemini-2.5-Pro and Claude-3.5-Sonnet [23][25]. Group 4: Industry Context - The demand for high-quality AI translation models has increased, necessitating adherence to cultural nuances and industry-specific language features [28][29]. - Traditional evaluation metrics are deemed insufficient for today's requirements, prompting the development of TransBench [31][32]. - Alibaba's Marco MT model has achieved significant usage, with an average daily call volume of 600 million, highlighting the importance of translation in global e-commerce [40][41].

Core Viewpoint - The article discusses the launch of TransBench, the first application-based AI translation evaluation ranking, aimed at standardizing translation quality assessments in the AI industry [1][5][32]. Group 1: TransBench Overview - TransBench is a collaborative effort by Alibaba International AI Business, Shanghai Artificial Intelligence Laboratory, and Beijing Language and Culture University [2]. - It introduces new evaluation metrics such as hallucination rate, cultural taboo words, and politeness norms, addressing common issues in large model translations [3][34]. - The evaluation system is open-source and has released its first assessment results, inviting AI translation institutions to participate [5][6][44]. Group 2: Evaluation Metrics - The evaluation framework categorizes data sets into three main types: "General Standards," "E-commerce Culture," and "Cultural Characteristics" [8]. - The ranking assesses translation capabilities across four dimensions: overall score, general standards, e-commerce culture, and cultural characteristics [9][11]. - The comprehensive score reflects the average performance across the three major dimensions, ensuring numerical consistency for comparison [11]. Group 3: Model Performance - In the English to other languages category, the top three models based on comprehensive and general standards scores are GPT-4o, DeepL Translate, and GPT-4-Turbo [16][14]. - For the e-commerce sector, DeepSeek-R1 ranks among the top performers, with Qwen2.5 models excelling in cultural characteristics [17][19]. - In the Chinese to other languages category, DeepSeek-V3 leads with a comprehensive score of 4.420, followed by Gemini-2.5-Pro and Claude-3.5-Sonnet [23][25]. Group 4: Industry Context - The AI translation model landscape is evolving, with increasing demands for models to meet cultural nuances and industry-specific language features [27][28]. - Traditional evaluation metrics are deemed insufficient for reflecting real-world requirements for semantic accuracy and user experience [29][31]. - The TransBench evaluation system is based on real user feedback from Alibaba's Marco MT, which has a daily usage of 600 million calls, making it the most utilized translation model in the e-commerce sector [40][41].