7 citations · 8 across the 10 of their papers we have counts for
12 papers
Unlocking Structured Pruning for Reasoning Language Models via Attention-Preserving MLP Trimming
Woosang Lim, Haneul Yoo, Vincent-Daniel Yun +1
Structured pruning reduces LLM inference costs by removing model components to produce smaller dense models. However, existing methods can cause reasoning performance to collapse o…
Prefill-Free Cross-Family KV Cache Transfer for Heterogeneous Multi-Agent LLMs
Vincent-Daniel Yun, Woosang Lim, Haneul Yoo +3
Recent multi-agent LLM systems increasingly combine heterogeneous models for specialized agent roles. However, text-based communication requires each receiver to prefill shared con…
Not What, But How: A Framework for Auditing LLM Responses across Positioning, Generalization, Anthropomorphism, and Maxims
Siddhesh Milind Pawar, Sarah Masud, Haneul Yoo +2
Large language models (LLMs) are being increasingly used to answer subjective, information-seeking questions, where users are sensitive to how responses are communicated, not just…
K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts
Nahyun Lee, Dongkeun Yoon, Guijin Son +12
Frontier model evaluations are shifting from foundational capabilities (e.g., instruction following and reasoning) toward compositional, agentic ones, but Korean agentic benchmarks…
Can Large Language Models Understand, Reason About, and Generate Code-Switched Text?
Genta Indra Winata, David Anugraha, Patrick Amadeus Irawan +15
Code-switching is a pervasive phenomenon in multilingual communication, yet the robustness of large language models (LLMs) in mixed-language settings remains insufficiently underst…
OLA: Output Language Alignment in Code-Switched LLM Interactions
Juhyun Oh, Haneul Yoo, Faiz Ghifari Haznitrama +1
Code-switching, alternating between languages within a conversation, is natural for multilingual users, yet poses fundamental challenges for large language models (LLMs). When a us…
From National Curricula to Cultural Awareness: Constructing Open-Ended Culture-Specific Question Answering Dataset
Haneul Yoo, Won Ik Cho, Geunhye Kim +1
Large language models (LLMs) achieve strong performance on many tasks, but their progress remains uneven across languages and cultures, often reflecting values latent in English-ce…
Gradual Code-Switching as Inference-Time Cross-Lingual Representational Alignment for LLMs
Haneul Yoo, Jiho Jin, Kyunghyun Cho +1
While large language models (LLMs) have achieved notable progress in multilingual settings, their performance remains uneven across languages as LLMs often rely on English-centric…
Code-Switching Curriculum Learning for Multilingual Transfer in LLMs
Haneul Yoo, Cheonbok Park, Sangdoo Yun +2
Large language models (LLMs) now exhibit near human-level performance in various tasks, but their performance drops drastically after a handful of high-resource languages due to th…
MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty
Yongjin Yang, Haneul Yoo, Hwaran Lee
Despite the massive advancements in large language models (LLMs), they still suffer from producing plausible but incorrect responses. To improve the reliability of LLMs, recent res…
Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding
Haneul Yoo, Yongjin Yang, Hwaran Lee
As large language models (LLMs) have advanced rapidly, concerns regarding their safety have become prominent. In this paper, we discover that code-switching in red-teaming queries…
HyperCLOVA X Technical Report
Kang Min Yoo, Jaegeun Han, Sookyo In +393
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. H…