9 papers
BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence
Sean Wu, Fredrik K. Gustafsson, Edward Phillips +3
Large language models (LLMs) often produce confident but incorrect answers in settings where abstention would be safer. Standard evaluation protocols, however, require a response a…
Semantic Self-Distillation for Language Model Uncertainty
Edward Phillips, Sean Wu, Fredrik K. Gustafsson +2
Large language models present challenges for principled uncertainty quantification, in part due to their complexity and the diversity of their outputs. Semantic dispersion, or the…
Optimization-Inspired Few-Shot Adaptation for Large Language Models
Boyan Gao, Xin Wang, Yibo Yang +1
Large Language Models (LLMs) have demonstrated remarkable performance in real-world applications. However, adapting LLMs to novel tasks via fine-tuning often requires substantial t…
SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking
Xingrun Xing, Boyan Gao, Zheng Zhang +5
Recent advancements in large language models (LLMs) with billions of parameters have improved performance in various applications, but their inference processes demand significant…
Is Temporal Prompting All We Need For Limited Labeled Action Recognition?
Shreyank N Gowda, Boyan Gao, Xiao Gu +1
Video understanding has shown remarkable improvements in recent years, largely dependent on the availability of large scaled labeled datasets. Recent advancements in visual-languag…
EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models
Xingrun Xing, Zheng Liu, Shitao Xiao +6
Modern large language models (LLMs) driven by scaling laws, achieve intelligence emergency in large model sizes. Recently, the increasing concerns about cloud costs, latency, and p…