In this work we investigate the potential of a large language model (LLM) to directly
comprehend visual signals without the necessity of fine-tuning on multi-modal datasets. The …

In the rapidly evolving field of large language models (LLMs), data augmentation (DA) has
emerged as a pivotal technique for enhancing model performance by diversifying training …

Artificial intelligence generated content (AIGC) technologies, with a predominance of large
language models (LLMs), have demonstrated remarkable performance improvements in …

Generative pre-trained transformer (GPT) models have revolutionized the field of natural
language processing (NLP) with remarkable performance in various tasks and also extend …

Generative language models have witnessed substantial traction, notably with the
introduction of refined models aimed at more coherent user-AI interactions—principally …

In the rapidly evolving field of large language models (LLMs), data augmentation (DA) has
emerged as a pivotal technique for enhancing model performance by diversifying training …

Instruction tuning of language models has demonstrated the ability to enhance model
generalization to unseen tasks via in-context learning using a few examples. However …

In this paper we, an epistemologist and a machine learning scientist, argue that we need to
pursue a novel area of philosophical research in AI–the ethics of belief for AI. Here we take …

This thesis presents an extensive study aimed at improving the efficacy of language models
in situations characterized by limited data resources, a prevalent challenge in the field of …