The integration of Large Language Models (LLMs) and Edge Intelligence (EI) introduces a
groundbreaking paradigm for intelligent edge devices. With their capacity for human-like …

Graph Neural Networks (GNNs) have received significant attention for demonstrating their
capability to handle graph data. However, they are difficult to be deployed in resource …

Multimedia online platforms (eg, Amazon, TikTok) have greatly benefited from the
incorporation of multimedia (eg, visual, textual, and acoustic) content into their personal …

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 …

This work provides the first theoretical analysis of training transformers to solve complex
problems by recursively generating intermediate states, analogous to fine-tuning for chain-of …

Graph plays an important role in representing complex relationships in real-world
applications such as social networks, biological data and citation networks. In recent years …

The success of Large Language Models (LLM) has led researchers to explore Multimodal
Large Language Models (MLLM) for unified visual and linguistic understanding. However …

Integrating and processing information from various sources or modalities are critical for
obtaining a comprehensive and accurate perception of the real world in autonomous …

Few-shot question answering (QA) aims at achieving satisfactory results on machine
question answering when only a few training samples are available. Recent advances …

While fusing heterogeneous open-source LLMs with varying architectures and sizes can
potentially integrate the strengths of different models, existing fusion methods face …