Accurately extracting entity relationships from text is challenging due to information redundancy, and errors can propagate misinformation with security implications. Document-level relation extraction (RE) presents a particular challenge due to its inherent complexity, requiring the synthesis of entity relationships distributed across multiple sentences within a document. Existing methods often rely on simply stacking network layers to address long-range dependencies, which can result in insufficient model robustness and errors when processing lengthy contexts. To address this, we propose RLNA-Net, a novel method that reframes document-level RE as a semantic segmentation task. Our model integrates a ResNet architecture with Layer Normalization to stabilize training and mitigate degradation caused by deep networks. Furthermore, we incorporate Multi-Head Self-Attention (MHSA) into the backbone to effectively capture diverse and long-range dependencies within the document. Comprehensive experiments on three benchmark datasets—DocRED, CDR, and GDA—demonstrate that RLNA-Net outperforms existing strong baseline models.
Federated learning is a distributed learning paradigm, where each client jointly trains a global model by only sharing gradients. Nevertheless, previous research has shown that the shared gradients can be used to privacy leakage, i.e., the so-called gradient inversion attack. Notably, existing attack studies have some generic shortcomings, such as insufficient theoretical guarantee and poor reconstruction when given difficult conditions. In our work, based on variational Bayesian theory, we propose a gradient inversion attack combined with the novel diffusion models, exploiting its remarkable expressiveness of image manifold to improve the inverted results. Compared with existing research, we provide elaborate theoretical analysis for our attack, showing its good adaptability in face of two real-world attack scenarios, namely as sample-level and user-level inversion. Through comprehensive validations under diverse datasets and realistic circumstances, we hope that our work can help understand and evaluate such privacy risk in federated learning.
As large language models (LLMs) scale, their inference-time computational cost and latency become increasingly prohibitive. Early-exit (EE) methodologies mitigate this issue by reducing the depth of effective inference, terminating the forward pass once the confidence in the prediction of the model exceeds a predefined threshold. However, existing EE approaches typically employ static thresholds, which can result in either premature exits that degrade predictive performance or unnecessarily deep computation that diminishes efficiency gains. To overcome these limitations, we propose DATEE, a Dynamic Adaptively Thresholded Early Exit framework based on a hierarchical trend sampling strategy. First, we develop a hierarchical trend-sampling mechanism. The first-stage sampling is conducted at shallow network layers to assess whether a predefined confidence threshold is met, while incurring minimal computational overhead. The second-stage sampling is then executed at deeper layers to capture and characterize confidence stabilization patterns. Second, we introduce a dynamically adaptive threshold method based on sampled feature representations, which jointly optimizes the confidence threshold and the initial sampling layer, thereby enabling coordinated and autonomous self-optimization. Experimental results on diverse natural language processing benchmarks show that DATEE stabilizes exits and improves inference efficiency by 1.1 × –4.9 × . Furthermore, it substantially reduces the average depth of inference and computational cost while preserving overall model performance.
The extensive generative capabilities of Large Language Models (LLMs) are causing the potential to increase abuse and misuse of AI-generated contents, which makes annotation and detection be critical tasks. Embedding watermarks into models is considered an effective approach that is imperceptible to humans but can be easily detected using specific algorithms. However, compared to the image and audio domains, the text domain possesses relatively limited redundancy. Most existing watermarking methods struggle to be effectively applied in scenarios requiring substantial embedding space, including low-entropy generation tasks and when it is necessary to embed multi-bit watermarks for advanced traceability. To address this challenge, in this work we propose a novel entropy-aware multi-bit watermarking framework. Specifically, by developing an entropy-aware filtering module, we estimate context entropy in real time to filter out invalid watermark spaces. Simultaneously, we integrate a multi-bit position allocation strategy to efficiently utilize the remaining space, thereby enabling trustworthy traceability of multi-bit watermark information in diverse generation scenarios. Comprehensive experiments demonstrate that our scheme improves text quality across various generation scenarios while maintaining high multi-bit decoding accuracy, providing a practical solution for the trustworthy traceability of LLMs.
Multi-agent reinforcement learning (MARL) faces significant challenges in unshaped scenarios where the state and action spaces dynamically change due to agent deaths or other environmental factors. Traditional methods either ignore these changes or employ hard masking techniques, leading to unstable learning and suboptimal policies. To address these limitations, we propose DP-QMIX, a novel approach that integrates a Death Prediction Model with the QMIX framework. Our method introduces three key innovations: (1) a multi-task DP-Model that jointly predicts agent survival probabilities and next states using LSTM networks, enabling progressive decay of dying agents’ action-values; (2) a self-weighted mixing network architecture that dynamically adjusts agent contributions based on survival probabilities; and (3) a soft elimination mechanism that stabilizes the learning environment by smoothly transitioning state spaces. Extensive experiments on StarCraft II micromanagement scenarios demonstrate that DP-QMIX achieves faster convergence (approximately 28 https://github.com/Vergissmeinncht/DP-QMIX .
