Humor generation is a challenging task in natural language generation that requires fluency, surprise, and cultural grounding. Although large language models (LLMs) show promise in English, little work addresses low-resource and culturally specific humor, such as in Korean. We propose **K-Humor**, a *dynamic multi-agent framework* for generating Korean jokes. Unlike static one-shot models, K-Humor integrates specialized agents for cultural awareness, audience simulation, editing, and punchline creation, which are coordinated through a dynamic iterative refinement loop. A human-data-grounded mechanism further guides the punchlines using authentic Korean jokes. Evaluation with automatic metrics, agent-internal diagnostics, LLM-as-a-judge, and human ratings shows that K-Humor consistently outperforms a single-prompt baseline and ablations in criteria such as fluency and cultural appropriateness. Although human-written jokes remain stronger overall, particularly on core creative dimensions, K-Humor demonstrates that dynamic, culturally grounded multi-agent systems can improve humor generation over standard LLM baselines in low-resource settings. Code is available at https://github.com/dxlabskku/k-humor.

As Large Language Models (LLMs) are increasingly used for content creation, detecting AI-generated text has become a critical challenge. Prior work has largely focused on English, leaving low-resource languages such as Korean underexplored. We propose an unsupervised detection framework that integrates two complementary signals: syntactic token cohesiveness (TOCSIN) and semantic regeneration similarity (SimLLM). To support evaluation, we construct a Korean pairwise dataset of 1,000 anchors with continuation- and regeneration-style generations and further assess performance across domains (news, research paper abstracts, essays) and model families (GPT-3.5 Turbo, GPT-4o, HyperCLOVA X, LLaMA-3-8B). Without any training, our ensemble achieves up to 0.963 F1 and 0.985 ROC-AUC, outperforming baselines. These results demonstrate that the combination of syntactic and semantic cues enables robust unsupervised detection in low-resource settings. Code available at https://github.com/dxlabskku/llm-detection-main.

Predicting video popularity is often framed as a supervised learning task, relying heavily on meta-information and aggregated engagement data. However, video popularity is shaped by complex cultural and social factors that such approaches often overlook. We argue that Large Language Models (LLMs), with their deep contextual awareness, can better capture these nuances. To bridge the gap between pixel-based video data and token-based LLMs, we convert frame-level visuals into sequential text representations using Vision-Language Models. This enables LLMs to process multimodal content—titles, frame-based descriptions, and captions—capturing both engagement intensity (view count) and geographic spread (number of countries where a video trends). On 13,639 popular videos, a supervised neural network using content embeddings achieves 80% accuracy, while our LLM-based approach reaches 82% without fine-tuning. Combining the neural network’s predictions with the LLM further improves accuracy to 85.5%. Moreover, the LLM generates interpretable, attribute-based explanations for its predictions. Manual validations confirm the quality of these hypotheses and address concerns about hallucinations in the video-to-text conversion process. Overall, our findings suggest that LLMs, equipped with text-based multimodal representations, offer a powerful, interpretable, and data-efficient solution for tasks requiring rich contextual insight, such as video popularity prediction.

Making decent multi-lingual sentence representations is critical to achieve high performances in cross-lingual downstream tasks. In this work, we propose a novel method to align multi-lingual embeddings based on the similarity of sentences measured by a pre-trained mono-lingual embedding model. Given translation sentence pairs, we train a multi-lingual model in a way that the similarity between cross-lingual embeddings follows the similarity of sentences measured at the mono-lingual teacher model. Our method can be considered as contrastive learning with soft labels defined as the similarity between sentences. Our experimental results on five languages show that our contrastive loss with soft labels far outperforms conventional constrastive loss with hard labels in various benchmarks for bitext mining tasks and STS tasks. In addition, our method outperforms existing multi-lingual embeddings including LaBSE, for Tatoeba dataset.