2 papers
cs.CL2026
RCPU: Rotation-Constrained Error Compensation for Structured Pruning of Large Language Models
Shuichiro Haruta, Kazunori Matsumoto, Zhi Li +2
In this paper, we propose a rotation-constrained compensation method to address the errors introduced by structured pruning of large language models (LLMs). LLMs are trained on mas…
cs.IR2025
WeaveRec: An LLM-Based Cross-Domain Sequential Recommendation Framework with Model Merging
Min Hou, Xin Liu, Le Wu +5
Cross-Domain Sequential Recommendation (CDSR) seeks to improve user preference modeling by transferring knowledge from multiple domains. Despite the progress made in CDSR, most exi…