Large Language Models (LLMs) are swiftly advancing in architecture and capability, and as
they integrate more deeply into complex systems, the urgency to scrutinize their security …

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 …

Large language models (LLMs) have demonstrated great success in various fields,
benefiting from their huge amount of parameters that store knowledge. However, LLMs still …

Large Language Models (LLMs) have demonstrated remarkable capabilities in important
tasks such as natural language understanding and language generation, and thus have the …

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 …

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 recently made significant strides in complex
reasoning tasks through the Chain-of-Thought technique. Despite this progress, their …

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 …

Contrastive learning has become a popular approach in natural language processing,
particularly for the learning of sentence embeddings. However, the discrete nature of natural …

Spurred by advancements in scale, large language models (LLMs) have demonstrated
strong few-shot learning ability via in-context learning (ICL). However, the performance of …