3 papers
cs.CL2026
FOCUS & RePAIR: Mitigating Text Degeneration via Token-Level Guidance for Pruned Large Language Models
Junyoung Lee, Sehyeon Park, Shinhyoung Jang +5
Pruning is a practical approach to compress large language models (LLMs), but it can amplify text degeneration, especially repetition loops, even when perplexity and task accuracy…
cs.CV2025
A Diffusion-Based Framework for Configurable and Realistic Multi-Storage Trace Generation
Seohyun Kim, Junyoung Lee, Jongho Park +3
We propose DiTTO, a novel diffusion-based framework for generating realistic, precisely configurable, and diverse multi-device storage traces. Leveraging advanced diffusion techniq…
cs.CL2025
FineScope : SAE-guided Data Selection Enables Domain Specific LLM Pruning and Finetuning
Chaitali Bhattacharyya, Hyunsei Lee, Junyoung Lee +3
Training large language models (LLMs) from scratch requires significant computational resources, driving interest in developing smaller, domain-specific LLMs that maintain both eff…