2 citations · 2 across the 5 of their papers we have counts for
6 papers
Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs
Yao Fu, Runchao Li, Xianxuan Long +4
Neural network pruning has emerged as a promising approach for deploying LLMs in low-resource scenarios while preserving downstream task performance. However, for the first time, w…
Quantized but Deceptive? A Multi-Dimensional Truthfulness Evaluation of Quantized LLMs
Yao Fu, Xianxuan Long, Runchao Li +5
Quantization enables efficient deployment of large language models (LLMs) in resource-constrained environments by significantly reducing memory and computation costs. While quantiz…
FAEDKV: Infinite-Window Fourier Transform for Unbiased KV Cache Compression
Runchao Li, Yao Fu, Mu Sheng +3
The efficacy of Large Language Models (LLMs) in long-context tasks is often hampered by the substantial memory footprint and computational demands of the Key-Value (KV) cache. Curr…
When Truthful Representations Flip Under Deceptive Instructions?
Xianxuan Long, Yao Fu, Runchao Li +4
Large language models (LLMs) tend to follow maliciously crafted instructions to generate deceptive responses, posing safety challenges. How deceptive instructions alter the interna…
Security and Privacy of Digital Twins for Advanced Manufacturing: A Survey
Alexander D. Zemskov, Yao Fu, Runchao Li +9
In Industry 4.0, the digital twin is one of the emerging technologies, offering simulation abilities to predict, refine, and interpret conditions and operations, where it is crucia…
Dynamic Self-Distillation via Previous Mini-batches for Fine-tuning Small Language Models
Yao Fu, Yin Yu, Xiaotian Han +4
Knowledge distillation (KD) has become a widely adopted approach for compressing large language models (LLMs) to reduce computational costs and memory footprints. However, the avai…