Supervised fine-tuning (SFT), supervised instruction tuning (SIT) and in-context learning
(ICL) are three alternative, de facto standard approaches to few-shot learning. ICL has …

Parameter-efficient fine-tuning (PEFT) using labeled task data can significantly improve the
performance of large language models (LLMs) on the downstream task. However, there are …

As the adoption of large language models increases and the need for per-user or per-task
model customization grows, the parameter-efficient fine-tuning (PEFT) methods, such as low …

The cultivation of expertise for large language models (LLMs) to solve tasks of specific areas
often requires special-purpose tuning with calibrated behaviors on the expected stable …

Parameter-efficient fine-tuning stands as the standard for efficiently fine-tuning large
language and vision models on downstream tasks. Specifically, the efficiency of low-rank …

Current state-of-the-art diffusion models employ U-Net architectures containing
convolutional and (qkv) self-attention layers. The U-Net processes images while being …

In multi-task learning, the conventional approach involves training a model on multiple tasks
simultaneously. However, the training signals from different tasks can interfere with one …

Different from the traditional semi-supervised learning paradigm that is constrained by the
close-world assumption, Generalized Category Discovery (GCD) presumes that the …

The availability of performant pre-trained models has led to a proliferation of fine-tuned
expert models that are specialized to particular domains. This has enabled the creation of …

As Generative AI becomes more prevalent, the vulnerability to security threats grows. This
study conducts a thorough exploration of red teaming methods within the domain of …