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Found 19 papers, 5 papers with code
Detecting Stealthy Backdoor Samples based on Intra-class Distance for Large Language Models
And then we perform TF-IDF clustering on these suspicious samples to identify the true poisoned samples based on the intra-class distance.
A Federated Splitting Framework for LLMs: Security, Efficiency, and Adaptability
However, it still faces significant challenges in security, efficiency, and adaptability: 1) embedding gradients are vulnerable to attacks, leading to reverse engineering of private data; 2) the autoregressive nature of LLMs means that federated split learning can only train and infer sequentially, causing high communication overhead; 3) fixed partition points lack adaptability to downstream tasks.
CodeBC: A More Secure Large Language Model for Smart Contract Code Generation in Blockchain
To address this challenge, we introduce CodeBC, a code generation model specifically designed for generating secure smart contracts in blockchain.
FineFilter: A Fine-grained Noise Filtering Mechanism for Retrieval-Augmented Large Language Models
Unlike these document-level operations, we treat noise filtering as a sentence-level MinMax optimization problem: first identifying potential clues from multiple documents, then ranking them by relevance, and finally retaining the minimum number of clues through truncation.
AdaComp: Extractive Context Compression with Adaptive Predictor for Retrieval-Augmented Large Language Models
Specifically, we first annotate the minimum top-k documents necessary for the RAG system to answer the current query as the compression rate and then construct triplets of the query, retrieved documents, and its compression rate.
HSF: Defending against Jailbreak Attacks with Hidden State Filtering
With the growing deployment of LLMs in daily applications like chatbots and content generation, efforts to ensure outputs align with human values and avoid harmful content have intensified.
MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models
Inspired by RLAIF, we train three kinds of reward models for the above metrics to achieve more efficient training.
Safely Learning with Private Data: A Federated Learning Framework for Large Language Model
An alternative, split learning, offloads most training parameters to the server while training embedding and output layers locally, making it more suitable for LLM.
Policy Optimization with Smooth Guidance Learned from State-Only Demonstrations
The sparsity of reward feedback remains a challenging problem in online deep reinforcement learning (DRL).
Adaptive trajectory-constrained exploration strategy for deep reinforcement learning
Deep reinforcement learning (DRL) faces significant challenges in addressing the hard-exploration problems in tasks with sparse or deceptive rewards and large state spaces.
An Evolution Kernel Method for Graph Classification through Heat Diffusion Dynamics
This approach incorporates the physics-based heat kernel and DropNode technique to transform each static graph into a sequence of temporal ones.
Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes
Specifically, low-frequency eigenmodes, which are considered sufficient to capture the essence of the functional network, contribute little to functional connectivity reconstruction in transmodal regions, resulting in structure-function decoupling along the unimodal-transmodal gradient.
Parameter Convex Neural Networks
Deep learning utilizing deep neural networks (DNNs) has achieved a lot of success recently in many important areas such as computer vision, natural language processing, and recommendation systems.
A novel feed rate scheduling method based on Sigmoid function with chord error and kinematics constraints
With the consideration of kinematic limitation and machining efficiency, a time-optimal feed rate adjustment algorithm is proposed to further adjust feed rate value at breaking points.
Research of Damped Newton Stochastic Gradient Descent Method for Neural Network Training
First-order methods like stochastic gradient descent(SGD) are recently the popular optimization method to train deep neural networks (DNNs), but second-order methods are scarcely used because of the overpriced computing cost in getting the high-order information.
A novel S-shape based NURBS interpolation with acc-jerk- Continuity and round-off error elimination
The algorithm consists of three modules: bidirectional scanning module, velocity scheduling module and round-off error elimination module.
Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network
Graphs are often used to organize data because of their simple topological structure, and therefore play a key role in machine learning.
Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs
The graph structure is a commonly used data storage mode, and it turns out that the low-dimensional embedded representation of nodes in the graph is extremely useful in various typical tasks, such as node classification, link prediction , etc.
Discriminative Embedding Autoencoder with a Regressor Feedback for Zero-Shot Learning
Zero-shot learning (ZSL) aims to recognize the novel object categories using the semantic representation of categories, and the key idea is to explore the knowledge of how the novel class is semantically related to the familiar classes.
