Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
However, it requires non-trivial efforts to implement these methods on different models. We …

Real-time learning concerns the ability of learning systems to acquire knowledge over time,
enabling their adaptation and generalization to novel tasks. It is a critical ability for …

Text-to-image generative models have garnered immense attention for their ability to
produce high-fidelity images from text prompts. Among these, Stable Diffusion distinguishes …

In the realm of Medical Visual Language Models (Med-VLMs), the quest for universal
efficient fine-tuning mechanisms remains paramount, especially given researchers in …

While large language models (LLMs) excel in world knowledge understanding, adapting
them to specific subfields requires precise adjustments. Due to the model's vast scale …

Large language models like ChatGPT have shown substantial progress in natural language
understanding and generation, proving valuable across various disciplines, including the …

Adapting pre-trained foundation models for various downstream tasks has been prevalent in
artificial intelligence. Due to the vast number of tasks and high costs, adjusting all …

Low-rank adaptation is a popular parameter-efficient fine-tuning method for large language
models. In this paper, we analyze the impact of low-rank updating, as implemented in LoRA …

Prediction methods inputting embeddings from protein Language Models (pLMs) have
reached or even surpassed state-of-the-art (SOTA) performance on many protein prediction …