2 citations · 5 across the 11 of their papers we have counts for
4 papers · 1 filter
Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets
Zhen Liu, Tim Z. Xiao, Weiyang Liu +2
While one commonly trains large diffusion models by collecting datasets on target downstream tasks, it is often desired to align and finetune pretrained diffusion models with some…
Can Large Language Models Understand Symbolic Graphics Programs?
Zeju Qiu, Weiyang Liu, Haiwen Feng +7
Against the backdrop of enthusiasm for large language models (LLMs), there is a growing need to scientifically assess their capabilities and shortcomings. This is nontrivial in par…
Verbalized Machine Learning: Revisiting Machine Learning with Language Models
Tim Z. Xiao, Robert Bamler, Bernhard Schölkopf +1
Motivated by the progress made by large language models (LLMs), we introduce the framework of verbalized machine learning (VML). In contrast to conventional machine learning (ML) m…
Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector
Andi Zhang, Tim Z. Xiao, Weiyang Liu +2
We revisit the likelihood ratio between a pretrained large language model (LLM) and its finetuned variant as a criterion for out-of-distribution (OOD) detection. The intuition behi…