3 papers
cs.CL2026
CPR for LLMs: Critical-Point Routing against Catastrophic Forgetting in Domain Adaptation
Kwangmin Ki, Yunhun Nam, Jongheon Jeong +1
Supervised fine-tuning (SFT) is the de facto standard for adapting large language models (LLMs) to target domains, but it often degrades the model's general capabilities, a phenome…
cs.LG2026
Diet Your LLM: Dimension-wise Global Pruning of LLMs via Merging Task-specific Importance Score
Jimyung Hong, Jaehyung Kim
Large language models (LLMs) have demonstrated remarkable capabilities, but their massive scale poses significant challenges for practical deployment. Structured pruning offers a p…
cs.LG2025
Learning from the Undesirable: Robust Adaptation of Language Models without Forgetting
Yunhun Nam, Jaehyung Kim, Jongheon Jeong
Language models (LMs) are often adapted through supervised fine-tuning (SFT) to specialize their capabilities for downstream tasks. However, in typical scenarios where the fine-tun…