Large language models (LLMs) are gaining increasing popularity in both academia and
industry, owing to their unprecedented performance in various applications. As LLMs …

Instruction tuning large language models (LLMs) remains a challenging task, owing to the
complexity of hyperparameter selection and the difficulty involved in evaluating the tuned …

Pre-training on large-scale datasets and then fine-tuning on downstream tasks have
become a standard practice in deep learning. However, pre-training data often contain label …

Contrastive Language-Image Pretraining (CLIP) has gained popularity for its remarkable
zero-shot capacity. Recent research has focused on developing efficient fine-tuning …

The" pre-training and fine-tuning" paradigm in addressing long-tailed recognition tasks has
sparked significant interest since the emergence of large vision-language models like the …

The rapid advancement of Large Language Models (LLMs) has introduced a new frontier in
natural language processing, particularly in understanding and processing long-context …

Foundation models are usually pre-trained on large-scale datasets and then adapted to
downstream tasks through tuning. However, the large-scale pre-training datasets, often …

When personalized federated learning (FL) meets large foundation models, new challenges
arise from various limitations in resources. In addition to typical limitations such as data …

In the evolving landscape of large language models (LLMs) tailored for software
engineering, the need for benchmarks that accurately reflect real-world development …

The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant
interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts …