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Found 18 papers, 4 papers with code
Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
Moreover, the challenge is further exacerbated by significant variation in data distributions and device capabilities across clients, which leads to intensified forgetting and degraded model generalization.
Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout
This work proposes DropPEFT, an innovative federated PEFT framework that employs a novel stochastic transformer layer dropout method, enabling devices to deactivate a considerable fraction of LLMs layers during training, thereby eliminating the associated computational load and memory footprint.
G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based Multi-agent Systems
Large Language Model (LLM)-based Multi-agent Systems (MAS) have demonstrated remarkable capabilities in various complex tasks, ranging from collaborative problem-solving to autonomous decision-making.
A Robust Federated Learning Framework for Undependable Devices at Scale
In this paper, we propose FLUDE to effectively deal with undependable environments.
Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics
By combining these two models with a proper ratio, FedGCF can achieve a comprehensive understanding of the graph data and deliver better performance, even under non-IID distributions.
NetSafe: Exploring the Topological Safety of Multi-agent Networks
Large language models (LLMs) have empowered nodes within multi-agent networks with intelligence, showing growing applications in both academia and industry.
Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model
To this end, we establish a causal framework for ST predictions, termed CaPaint, which targets to identify causal regions in data and endow model with causal reasoning ability in a two-stage process.
Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey
Compared to traditional sentiment analysis, which only considers text, multimodal sentiment analysis needs to consider emotional signals from multimodal sources simultaneously and is therefore more consistent with the way how humans process sentiment in real-world scenarios.
All Nodes are created Not Equal: Node-Specific Layer Aggregation and Filtration for GNN
This assumption often poses challenges for many GNNs working with heterophilic graphs.
CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks
Credit card fraud poses a significant threat to the economy.
Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence
Emotional Intelligence (EI), consisting of emotion perception, emotion cognition and emotion expression, plays the critical roles in improving user interaction experience for the current large language model (LLM) based conversational general AI assistants.
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models
Existing methods devise the learning module to acquire task-specific knowledge with parameter-efficient tuning (PET) block and the selection module to pick out the corresponding one for the testing input, aiming at handling the challenges of catastrophic forgetting and knowledge transfer in CL.
Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model
Efficiently modeling spatio-temporal (ST) physical processes and observations presents a challenging problem for the deep learning community.
An Early Evaluation of GPT-4V(ision)
In this paper, we evaluate different abilities of GPT-4V including visual understanding, language understanding, visual puzzle solving, and understanding of other modalities such as depth, thermal, video, and audio.
The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field
Despite Graph Neural Networks demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with over-fitting and over-smoothing as they go deeper as models of computer vision realm.
TransESC: Smoothing Emotional Support Conversation via Turn-Level State Transition
Specifically, we construct the state transition graph with a two-step way, named transit-then-interact, to grasp such three types of turn-level transition information.
Is ChatGPT Equipped with Emotional Dialogue Capabilities?
This report presents a study on the emotional dialogue capability of ChatGPT, an advanced language model developed by OpenAI.
EZLDA: Efficient and Scalable LDA on GPUs
LDA is a statistical approach for topic modeling with a wide range of applications.
