5 papers · 1 filter
Benchmarking Trustworthiness of SLMs: Pre-trained vs. Compressed
Haokun Lin, Kaijie Zhu, Haobo Xu +4
Small Language Models (SLMs) have emerged as a more efficient alternative to traditional Large Language Models (LLMs), offering promising potential in resource-constrained scenario…
Before Thinking, Learn to Decide: Proactive Routing for Efficient Visual Reasoning
Yinan Zhou, Haokun Lin, Yichen Wu +7
Large multimodal models have achieved strong reasoning on complex visual tasks, but their inference efficiency is often restricted by long chains of thought. A promising solution i…
MedREK: Retrieval-Based Editing for Medical LLMs with Key-Aware Prompts
Shujun Xia, Haokun Lin, Yichen Wu +9
LLMs hold great promise for healthcare applications, but the rapid evolution of medical knowledge and errors in training data often cause them to generate outdated or inaccurate in…
Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs
Haokun Lin, Haobo Xu, Yichen Wu +6
Recent advances in diffusion large language models (dLLMs) have introduced a promising alternative to autoregressive (AR) LLMs for natural language generation tasks, leveraging ful…
DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs
Haokun Lin, Haobo Xu, Yichen Wu +6
Quantization of large language models (LLMs) faces significant challenges, particularly due to the presence of outlier activations that impede efficient low-bit representation. Tra…