给意第绪语造了个AI，但更值钱的是方法
意第绪语有了第一个开源大模型，8B参数，能翻译、做语法分析、抽取信息。但真正值得看的不是这个模型本身，而是它背后的思路：他们先建了一个高质量语料库，专门挑当代网络文本和文学作品，过滤掉机器翻译和错分类的噪音，再用这个语料去微调Llama 3.1。结果模型不仅分数更高，还真的抓住了意第绪语特有的词汇和形态规律——而通用多语模型在这些地方会犯错。这暴露了一个更大的问题：网上那些海量多语数据，对低资源语言其实是噪音。你明天用不上它，但如果你关心AI怎么对待小语种，这是个信号。
📄 原文摘要(英文)
We present MameLoshnLM, the first open-source 8B-parameter language model built specifically for Yiddish. Despite Yiddish's rich textual tradition, its limited digital presence and the scarcity of reliable evaluation resources have constrained progress in Yiddish language modeling. Existing multilingual corpora and benchmarks are often poor proxies for the language, containing substantial amounts of noisy, machine-translated, and misclassified text. We address these gaps by introducing Oytser, a high-quality Yiddish pretraining corpus that combines contemporary web-native sources with literary materials, and Kashes, a multi-task benchmark spanning translation, linguistic analysis, information extraction, and language understanding. Using these resources, we continue pretraining Llama 3.1 8B to obtain MameLoshnLM. Across the tasks in the benchmark, MameLoshnLM outperforms open baselines of similar scale. Our analyses show that these gains are not only quantitative: relative to general-purpose multilingual models, MameLoshnLM better captures language-defining lexical and morphological patterns, pointing to a broader failure mode of noisy web-scale multilingual data for low-resource languages. Our results provide both a foundation for Yiddish NLP and a practical template for language model development in historically rich but digitally underrepresented languages.