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 have demonstrated remarkable capabilities, but their performance
is heavily reliant on effective prompt engineering. Automatic prompt optimization (APO) …

Solving complex real-world tasks requires cycles of actions and observations. This is
particularly true in science, where tasks require many cycles of analysis, tool use, and …

This position paper proposes a data-centric viewpoint of AI research, focusing on large
language models (LLMs). We start by making the key observation that data is instrumental in …

This position paper proposes a data-centric viewpoint of AI research, focusing on large
language models (LLMs). We start by making a key observation that data is instrumental in …

Optimal prompt selection is crucial for maximizing large language model (LLM) performance
on downstream tasks. As the most powerful models are proprietary and can only be invoked …

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 …