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 …

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) augmented with external data have demonstrated
remarkable capabilities in completing real-world tasks. Techniques for integrating external …

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 …

Knowledge Graph Completion (KGC) is crucial for addressing knowledge graph
incompleteness and supporting downstream applications. Many models have been …

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 …

Large language models (LLMs) have emerged as powerful tools for many AI problems and
exhibit remarkable in-context learning (ICL) capabilities. Compositional ability, solving …

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 …