arXiv cs.CL· SungHo Kim, Eojin Jeon, SangKeun Lee·· 3 天前AI 评分26
KoAGE：大语言模型能否识别、学习并创造韩语新词？
KoAGE: Do Large Language Models Know, Learn, and Coin Korean Neologisms?
AI 导读
研究团队推出 NeoTrace 框架与 KoAGE 基准，用于评估大语言模型对韩语新词的掌握。该基准含 4,271 条新词条目，覆盖三项习得任务与两项生成任务，其中生成任务首次在原始语境下对比模型造词与真实新词。在 21 个模型上，词汇记忆与语境推断出现分化：五模型时序分析中，模型仅能定义 13-30% 的截止年份新词，却可从单次用法推断含义。
正文 · 原文
Abstract:Korean neologisms arise through diverse mechanisms, a demanding test of language models beyond lexical memorization. To enable systematic evaluation, we introduce NeoTrace, an automated, rerunnable framework that collects and dates neologisms across genres. Its 4,271 entries support KoAGE, a cutoff-aware benchmark with three acquisition and two generation tasks---the latter, to our knowledge, the first to compare model coinages with attested neologisms under their original materials and community context. Across 21 models, lexical recall diverges from contextual inference: in a five-model temporal analysis, models define only 13-30% of cutoff-year words, yet can infer meaning from one usage. However, structural understanding remains limited: even correct definitions barely improve formation analysis of pre-cutoff neologisms. Given word-building materials, single models often follow human formation mechanisms, whereas multi-agent coinage favors abbreviation and depends on interaction framing. KoAGE thus separates what models know, learn from use, and coin.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.19399 [cs.CL] |
| (or arXiv:2610.19399v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19399 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: SungHo Kim [view email]
[v1]
Wed, 14 Oct 2026 01:49:19 UTC (2,009 KB)
来源：arXiv cs.CL · arxiv.org