Large Language Models (LLMs), which bridge the gap between human language
understanding and complex problem-solving, achieve state-of-the-art performance on …

Neural style transfer (NST) generates new images by combining the style of one image with
the content of another. However, unauthorized NST can exploit artwork, raising concerns …

Federated Learning (FL) has gained prominence as a method for training models on edge
computing devices, enabling the preservation of data privacy by eliminating the need to …

The advancement of Large Language Models (LLMs) has significantly impacted various
domains, including Web search, healthcare, and software development. However, as these …

This paper provides a comprehensive evaluation and comparison of current reinforcement
learning methods. By analyzing the strengths and weaknesses of the main methods, such as …

Prompt-based learning, a paradigm that creates a bridge between pre-training and fine-
tuning stages, has proven to be highly effective concerning various NLP tasks, particularly in …

Parameter-efficient fine-tuning (PEFT) has become a key training strategy for large language
models. However, its reliance on fewer trainable parameters poses security risks, such as …

Parameter-efficient fine-tuning (PEFT) can bridge the gap between large language models
(LLMs) and downstream tasks. However, PEFT has been proven vulnerable to malicious …

Sentiment analysis on social media platforms is a critical area of research for understanding
public opinion, particularly during significant events like elections. This paper presents a …

As the large vision language models and embodied intelligent robotic networks continue to
advance at a remarkable pace, particularly in applications spanning smart cities, power …