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Thunder-DeID+ 提出一种边界感知的韩语去标识化方法，通过改进的 BPE 分词器与三层 PII 分类体系，将法院判决书上的 Strict Typed F1 从 0.76-0.77 提升至 0.87-0.88，并在微调后达到 0.93，实现向更广泛敏感文档的迁移。
The paper introduces NeoTrace and KoAGE, a cutoff-aware benchmark for evaluating how large language models know, learn, and coin Korean neologisms, finding that lexical recall diverges from contextual inference across 21 models.
This paper presents the construction of a Korean evaluation-annotated corpus (EVAD) for fine-grained aspect-based sentiment analysis in e-commerce reviews using Semi-Automatic Symbolic Propagation. It evaluates KoBERT and KcBERT models on the dataset, achieving high F1 scores in aspect-value pair recognition.
This paper presents a method for generating large-scale, labeled training datasets for legal chatbots in Korean using Local Grammar Graphs, achieving 91% F1-score with a DIET classifier.
This paper presents a systematic benchmark of token pruning—a compression technique that removes tokens and embeddings for irrelevant languages—applied to Korean-centric LLM tasks. The study evaluates popular multilingual models (Qwen3, Gemma-3, Llama-3, Aya) across different vocabulary configurations and finds that token pruning significantly improves generation stability and reduces memory footprint for domain-specific deployments.