3 papers
cs.CL2026
GRASS: Gradient-based Adaptive Layer-wise Importance Sampling for Memory-efficient Large Language Model Fine-tuning
Kaiyuan Tian, Yu Tang, Gongqingjian Jiang +5
Full-parameter fine-tuning of large language models is constrained by substantial GPU memory requirements. Low-rank adaptation methods mitigate this challenge by updating only a su…
cs.CL2026
Let the Agent Search: Autonomous Exploration Beats Rigid Workflows in Temporal Question Answering
Xufei Lv, Jiahui Yang, Haoyuan Sun +5
Temporal Knowledge Graph Question Answering (TKGQA) is challenging because it requires multi-hop reasoning under complex temporal constraints. Recent LLM-based approaches have impr…
cs.CL2025
Don't Half-listen: Capturing Key-part Information in Continual Instruction Tuning
Yongquan He, Wenyuan Zhang, Xuancheng Huang +5
Instruction tuning for large language models (LLMs) can drive them to produce results consistent with human goals in specific downstream tasks. However, the process of continual in…