翻译模型被人类出题人考倒了
翻译界的「高考」正在失效：标准测试题快被 AI 刷穿了，自动评分又容易被骗，连人工评分都不可靠。于是研究者换了个思路——不再出「正常句子」，而是专门收集人类精心设计、能难倒最强翻译模型的例子，涵盖文本、图片、音频和视频，每个例子还配了人工写的「判卷规则」，专门指出模型会在哪里翻车。这不是你明天能用的工具，但它意味着翻译 AI 的「考试」从刷题转向了找茬，以后模型强不强，得看它能不能过人类出的刁钻题。
📄 原文摘要(英文)
For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, standard benchmarks for machine translation are approaching saturation. Further, automatic translation metrics are unreliable, vulnerable to reward-hacking, and provide unactionable assessments. Even gold human evaluation is not problem-free, because it often lacks reproducibility, objectivity, and scalability. Overall, this prevents us from tracking objective progress in the field and identifying pathways for improvement. We introduce the Last Translation Benchmark, a collection of human-authored and peer-reviewed examples (texts, images, audio, videos) that break leading machine translation models. We also present a new evaluation approach: each example comes with handcrafted verification rules describing concrete failure cases on that example, therefore allowing reliable and actionable future evaluation. The Last Translation Benchmark is a live dataset that accepts ongoing contributions. The latest version is LTBv1, containing accepted contributions prior to September 1st 2026, with future releases planned as new data is continuously collected.