5 citations · 8 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering
Zhihua Wen, Zhiliang Tian, Zexin Jian +5
Large Language Models (LLMs) are widely used for knowledge-seeking yet suffer from hallucinations. The knowledge boundary (KB) of an LLM limits its factual understanding, beyond wh…
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…
Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models
Yifu Gao, Linbo Qiao, Zhigang Kan +3
Temporal knowledge graph question answering (TKGQA) poses a significant challenge task, due to the temporal constraints hidden in questions and the answers sought from dynamic stru…