Among the widely used parameter-efficient fine-tuning (PEFT) methods, LoRA and its
variants have gained considerable popularity because of avoiding additional inference …

Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-
learning, or few-shot learning, aims to effectively train a model using only a small amount of …

Soft prompt tuning achieves superior performances across a wide range of few-shot tasks.
However, the performances of prompt tuning can be highly sensitive to the initialization of …

With the continuous growth of large language models, the process of fine-tuning these
models for new tasks has become increasingly parameter-intensive. Prompt tuning, a …

Compositional zero-shot learning (CZSL) task aims to recognize unseen compositional
visual concepts, eg., sliced tomatoes, where the model is learned only from the seen …

Target-speaker automatic speech recognition (ASR) aims to transcribe the desired speech
of a target speaker from multi-talker overlapped utterances. Most of the existing target …

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 …

Recent advances in fine-tuning large language models (LLMs) have greatly enhanced their
usage in domain-specific tasks. Despite the success, fine-tuning continues to rely on …

Ensuring awareness of fairness and privacy in Large Language Models (LLMs) is critical.
Interestingly, we discover a counter-intuitive trade-off phenomenon that enhancing an LLM's …

Large language models (LLMs) and vision language models (VLMs) demonstrate excellent
performance on a wide range of tasks by scaling up parameter counts from O (10^ 9) to O …