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

In software development, resolving the emergent issues within GitHub repositories is a
complex challenge that involves not only the incorporation of new code but also the …

Large language models~(LLMs) are instruction followers, but it can be challenging to find
the best instruction for different situations, especially for black-box LLMs on which …

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 …

Large Language Models (LLMs) represent a class of deep learning models adept at
understanding natural language and generating coherent responses to various prompts or …

While fine-tuning of pre-trained language models generally helps to overcome the lack of
labelled training samples, it also displays model performance instability. This instability …

The large models, as predicted by scaling raw forecasts, have made groundbreaking
progress in many fields, particularly in natural language generation tasks, where they have …

Time series data is prevalent across numerous fields, necessitating the development of
robust and accurate forecasting models. Capturing patterns both within and between …

This thesis advances the field of efficient knowledge transfer and adaptation in the realm of
speech processing. It is structured to address the limitations of transfer learning in …

In-context learning (ICL) has proven to be adept at adapting large language models (LLMs)
to downstream tasks without parameter updates, based on a few demonstration examples …