Large language models (LLMs) have shown remarkable generalization capability with
exceptional performance in various language modeling tasks. However, they still exhibit …

Large models, encompassing large language and diffusion models, have shown
exceptional promise in approximating human-level intelligence, garnering significant …

Few-shot extractive question answering (EQA) is a challenging task in natural language
processing, whose current methods are mainly based on pretrained language models …

In Multimodal Language Models (MLMs), the cost of manually annotating high-quality image-
text pair data for fine-tuning and alignment is extremely high. While existing multimodal data …

In the realm of fine-tuning pre-trained language models in modern prompt engineering, we
introduce a novel graph-theoretical approach to address the resource-intensive challenges …

Vision-language modeling is a crucial subfield of AI that focuses on jointly learning and
representing image and text data, often using one modality to enhance understanding of the …

Relational data, especially graphs where entities are represented as nodes and the
relations connecting them are denoted as edges, have become a common language for …

The recent development of artificial intelligence (AI) has facilitated the prosperity of
foundation models, such as large language models (LLMs) and vision models. The …