1 citations · 6 across the 24 of their papers we have counts for
6 papers · 1 filter
AWP: Activation-Aware Weight Pruning and Quantization with Projected Gradient Descent
Jing Liu, Toshiaki Koike-Akino, Ye Wang +2
To address the enormous size of Large Language Models (LLMs), model compression methods, such as quantization and pruning, are often deployed, especially on edge devices. In this w…
TuneComp: Joint Fine-tuning and Compression for Large Foundation Models
Xiangyu Chen, Jing Liu, Ye Wang +4
To reduce model size during post-training, compression methods, including knowledge distillation, low-rank approximation, and pruning, are often applied after fine-tuning the model…
-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts
Toshiaki Koike-Akino, Jing Liu, Ye Wang
To tackle the huge computational demand of large foundation models, activation-aware compression techniques without retraining have been introduced. However, since these rely on ca…
LatentLLM: Attention-Aware Joint Tensor Compression
Toshiaki Koike-Akino, Xiangyu Chen, Jing Liu +4
Modern foundation models such as large language models (LLMs) and large multi-modal models (LMMs) require a massive amount of computational and memory resources. We propose a new f…
Winning Big with Small Models: Knowledge Distillation vs. Self-Training for Reducing Hallucination in Product QA Agents
Ashley Lewis, Michael White, Jing Liu +3
The deployment of Large Language Models (LLMs) in customer support is constrained by hallucination (generating false information) and the high cost of proprietary models. To addres…
Smoothed Embeddings for Robust Language Models
Ryo Hase, Md Rafi Ur Rashid, Ashley Lewis +4
Improving the safety and reliability of large language models (LLMs) is a crucial aspect of realizing trustworthy AI systems. Although alignment methods aim to suppress harmful con…