3 papers
cs.CV2026
video-SALMONN-R: Learning to ReWatch, ReAsk, and ReAnswer for Efficient Video Understanding
Yixuan Li, Guangzhi Sun, Yudong Yang +1
Video large language models (LLMs) are often constrained by computation and memory budgets, leading them to use reduced frame rates and spatial resolutions, which may cause them to…
cs.CY2026
Uncertainty-based Debiasing and Unlearning for Decontamination
Guangzhi Sun, Xiao Zhan, Mark Gales
Benchmark-based evaluation is the dominant paradigm for assessing large language model (LLM) capabilities, yet data contamination inflates reported performance and undermines fair…
cs.LG2025
Evaluating Sparse Autoencoders for Monosemantic Representation
Moghis Fereidouni, Muhammad Umair Haider, Peizhong Ju +1
A key barrier to interpreting large language models is polysemanticity, where neurons activate for multiple unrelated concepts. Sparse autoencoders (SAEs) have been proposed to mit…