10 citations · 13 across the 12 of their papers we have counts for
4 papers · 1 filter
Velocitune: A Velocity-based Dynamic Domain Reweighting Method for Continual Pre-training
Zheheng Luo, Xin Zhang, Xiao Liu +4
It is well-known that a diverse corpus is critical for training large language models, which are typically constructed from a mixture of various domains. In general, previous effor…
Enhancing Large Language Model Performance with Gradient-Based Parameter Selection
Haoling Li, Xin Zhang, Xiao Liu +4
Large language models (LLMs) have revolutionized lots of fields of research. Although it is well-known that fine-tuning is essential for enhancing the capabilities of LLMs, existin…
InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions
Yifan Wang, Yafei Liu, Chufan Shi +4
Instruction tuning effectively optimizes Large Language Models (LLMs) for downstream tasks. Due to the changing environment in real-life applications, LLMs necessitate continual ta…
Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion
Ruilin Luo, Tianle Gu, Haoling Li +4
Temporal Knowledge Graph Completion (TKGC) is a complex task involving the prediction of missing event links at future timestamps by leveraging established temporal structural know…