A major factor in the recent success of large language models is the use of enormous and
ever-growing text datasets for unsupervised pre-training. However, naively training a model …

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 …

As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can
offer reliable and up-to-date external knowledge, providing huge convenience for numerous …

Prompt engineering is a technique that involves augmenting a large pre-trained model with
task-specific hints, known as prompts, to adapt the model to new tasks. Prompts can be …

Large language models (LLMs) can learn to perform a wide range of natural language tasks
from just a handful of in-context examples. However, for generating strings from highly …

In-context learning (ICL) emerges as a promising capability of large language models
(LLMs) by providing them with demonstration examples to perform diverse tasks. However …

Large language models (LLMs) have demonstrated their ability to learn in-context, allowing
them to perform various tasks based on a few input-output examples. However, the …

Recently, Chain-of-Thought (CoT) prompting has delivered success on complex reasoning
tasks, which aims at designing a simple prompt like``Let's think step by step''or multiple in …

Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are
often publicly unavailable and their immense sizes make the models difficult to be tuned with …

Large Language Models (LLMs) are popular for their impressive abilities, but the need for
model-specific fine-tuning or task-specific prompt engineering can hinder their …