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 …

Entity resolution (ER) is an important data integration task with a wide spectrum of
applications. The state-of-the-art solutions on ER rely on pre-trained language models …

Instruction Fine-tuning~(IFT) is a critical phase in building large language models~(LLMs).
Previous works mainly focus on the IFT's role in the transfer of behavioral norms and the …

Large language models (LLMs) have shown impressive capabilities in real-world
applications. The capability of in-context learning (ICL) allows us to adapt an LLM to …

Utilizing user profiles to personalize Large Language Models (LLMs) has been shown to
enhance the performance on a wide range of tasks. However, the precise role of user …

In this paper, we leverage in-context learning (ICL) paradigm to handle few-shot aspect-
based sentiment analysis (ABSA). Previous works first rank candidate examples by some …

Large language models (LLMs) have shown promising abilities of in-context learning (ICL),
adapting swiftly to new tasks with only few-shot demonstrations. However, current few-shot …

This paper presents a new tool learning dataset Seal-Tools, which contains self-instruct API-
like tools. Seal-Tools not only offers a large number of tools, but also includes instances …