8 papers · 1 filter
EMSEdit: Efficient Multi-Step Meta-Learning-based Model Editing
Xiaopeng Li, Shasha Li, Xi Wang +7
Large Language Models (LLMs) power numerous AI applications, yet updating their knowledge remains costly. Model editing provides a lightweight alternative through targeted paramete…
Rethinking Residual Distribution in Locate-then-Edit Model Editing
Xiaopeng Li, Shanwen Wang, Shasha Li +4
Model editing enables targeted updates to the knowledge of large language models (LLMs) with minimal retraining. Among existing approaches, locate-then-edit methods constitute a pr…
LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deployment
Binrui Zeng, Bin Ji, Xiaodong Liu +7
As Large Language Models (LLMs) demonstrate exceptional performance across various domains, deploying LLMs on edge devices has emerged as a new trend. Quantization techniques, whic…
How to Alleviate Catastrophic Forgetting in LLMs Finetuning? Hierarchical Layer-Wise and Element-Wise Regularization
Shezheng Song, Hao Xu, Jun Ma +5
Large Language Models (LLMs) exhibit strong general language capabilities. However, fine-tuning these models on domain-specific tasks often leads to catastrophic forgetting, where…
SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding Altering
Xiaopeng Li, Shasha Li, Shezheng Song +8
The general capabilities of large language models (LLMs) make them the infrastructure for various AI applications, but updating their inner knowledge requires significant resources…
How to Bridge the Gap between Modalities: Survey on Multimodal Large Language Model
Shezheng Song, Xiaopeng Li, Shasha Li +5
We explore Multimodal Large Language Models (MLLMs), which integrate LLMs like GPT-4 to handle multimodal data, including text, images, audio, and more. MLLMs demonstrate capabilit…