LLMs是否知道卢森堡语借词？探测低资源多语言模型中的词汇新词
Do LLMs Know What Luxembourgish Borrows? Probing Lexical Neology in Low-Resource Multilingual Models
AI总结：
本文通过LexNeo-Bench基准测试，探讨了低资源多语言模型在词汇借词识别中的表现，发现通过构建语言知识图谱可以显著提升模型的借词分类准确率，表明词汇资源对LLM评估具有结构化上下文的作用。
摘要：
大型语言模型（LLMs）越来越多地用于小接触语言的写作辅助，但不清楚它们是否尊重社区在词汇借词和新词方面的规范。我们引入LexNeo-Bench，一个包含3,050个实例的令牌级基准，来源于LuxBorrow，一个大规模的卢森堡语新闻语料库，其中目标令牌被标记为本土词或法语、德语或英语借词。使用此基准，我们对三种多语言LLM在34种提示设置下进行测试，涉及两种任务：借词类型分类和二元词汇创新代理（借词与本土词）。在无外部上下文的情况下，模型在借词分类上的表现仅略高于随机猜测，因此我们构建了一个语言知识图谱，编码了供体语言、形态模式和词汇类比，并将实例特定的子图注入提示中。知识图谱提示将借词分类准确率从25-35%提升到71-81%，大幅缩小了小模型和大模型之间的差距，同时使新词检测困难且对少样本设计敏感。我们的结果表明，词汇意识提示对低资源接触语言中的稳健借词判断非常有益，且词汇资源可以作为LLM评估的结构化上下文。本研究在ENEOLI COST行动中进行，并探讨了多语言卢森堡语数据中的借词作为词汇创新的形式。
英文摘要：
Large language models (LLMs) are increasingly used for writing assistance in small contact languages, yet it is unclear whether they respect community norms around lexical borrowing and neology. We introduce LexNeo-Bench, a 3{,}050-instance token-level benchmark derived from LuxBorrow, a large-scale Luxembourgish news corpus, where target tokens are labelled as native or as French, German, or English borrowings. Using this benchmark, we probe three multilingual LLMs across 34 prompt settings on two tasks: borrowing type classification and a binary lexical-innovation proxy (borrowing versus native). Without external context, models perform only slightly above chance on borrowing classification, so we construct a linguistic knowledge graph that encodes donor language, morphological patterns, and lexical analogues, and inject instance-specific subgraphs into the prompt. Knowledge-graph prompts raise borrowing classification accuracy from 25 -- 35\% up to 71 -- 81\% and largely close the gap between small and large models, while leaving neology detection difficult and sensitive to few-shot design. Our results show that lexicon-aware prompting is highly beneficial for robust borrowing judgments in low-resource contact languages and that lexical resources can serve as structured context for LLM evaluation. This study was carried out within the ENEOLI COST Action and examines borrowing as a form of lexical innovation in multilingual Luxembourgish data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
