128 citations · 927 across the 99 of their papers we have counts for
12 papers · 2 filters
ASLoRA: Adaptive Sharing Low-Rank Adaptation Across Layers
Junyan Hu, Xue Xiao, Mengqi Zhang +4
As large language models (LLMs) grow in size, traditional full fine-tuning becomes increasingly impractical due to its high computational and storage costs. Although popular parame…
Uncovering Overfitting in Large Language Model Editing
Mengqi Zhang, Xiaotian Ye, Qiang Liu +3
Knowledge editing has been proposed as an effective method for updating and correcting the internal knowledge of Large Language Models (LLMs). However, existing editing methods oft…
MACPO: Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization
Yougang Lyu, Lingyong Yan, Zihan Wang +4
As large language models (LLMs) are rapidly advancing and achieving near-human capabilities on specific tasks, aligning them with human values is becoming more urgent. In scenarios…
Enhancing Multi-hop Reasoning through Knowledge Erasure in Large Language Model Editing
Mengqi Zhang, Bowen Fang, Qiang Liu +4
Large language models (LLMs) face challenges with internal knowledge inaccuracies and outdated information. Knowledge editing has emerged as a pivotal approach to mitigate these is…
MEFT: Memory-Efficient Fine-Tuning through Sparse Adapter
Jitai Hao, WeiWei Sun, Xin Xin +4
Parameter-Efficient Fine-tuning (PEFT) facilitates the fine-tuning of Large Language Models (LLMs) under limited resources. However, the fine-tuning performance with PEFT on comple…
Autonomous Workflow for Multimodal Fine-Grained Training Assistants Towards Mixed Reality
Jiahuan Pei, Irene Viola, Haochen Huang +9
Autonomous artificial intelligence (AI) agents have emerged as promising protocols for automatically understanding the language-based environment, particularly with the exponential…