The Hidden Power of Normalization: Exponential Capacity Control in Deep Neural Networks

arXiv:2511.00958v1 Announce Type: cross Abstract: Normalization methods are fundamental components of modern deep neural networks (DNNs). Empirically, they are known to stabilize optimization dynamics and

FeNN-DMA: A RISC-V SoC for SNN acceleration

arXiv:2511.00732v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are a promising, energy-efficient alternative to standard Artificial Neural Networks (ANNs) and are particularly well-suited to

Parameter Interpolation Adversarial Training for Robust Image Classification

arXiv:2511.00836v1 Announce Type: cross Abstract: Though deep neural networks exhibit superior performance on various tasks, they are still plagued by adversarial examples. Adversarial training has

Region-Aware Reconstruction Strategy for Pre-training fMRI Foundation Model

arXiv:2511.00443v1 Announce Type: cross Abstract: The emergence of foundation models in neuroimaging is driven by the increasing availability of large-scale and heterogeneous brain imaging datasets.

FlashEVA: Accelerating LLM inference via Efficient Attention

arXiv:2511.00576v1 Announce Type: cross Abstract: Transformer models have revolutionized natural language processing, achieving state-of-the-art performance and demonstrating remarkable scalability. However, their memory demands, particularly due

Retrieval-Augmented Generation-based Relation Extraction

October 29, 2025

arXiv:2404.13397v2 Announce Type: replace-cross
Abstract: Information Extraction (IE) is a transformative process that converts unstructured text data into a structured format by employing entity and relation extraction (RE) methodologies. The identification of the relation between a pair of entities plays a crucial role within this framework. Despite the existence of various techniques for relation extraction, their efficacy heavily relies on access to labeled data and substantial computational resources. In addressing these challenges, Large Language Models (LLMs) emerge as promising solutions; however, they might return hallucinating responses due to their own training data. To overcome these limitations, Retrieved-Augmented Generation-based Relation Extraction (RAG4RE) in this work is proposed, offering a pathway to enhance the performance of relation extraction tasks.
This work evaluated the effectiveness of our RAG4RE approach utilizing different LLMs. Through the utilization of established benchmarks, such as TACRED, TACREV, Re-TACRED, and SemEval RE datasets, our aim is to comprehensively evaluate the efficacy of our RAG4RE approach. In particularly, we leverage prominent LLMs including Flan T5, Llama2, and Mistral in our investigation. The results of our study demonstrate that our RAG4RE approach surpasses performance of traditional RE approaches based solely on LLMs, particularly evident in the TACRED dataset and its variations. Furthermore, our approach exhibits remarkable performance compared to previous RE methodologies across both TACRED and TACREV datasets, underscoring its efficacy and potential for advancing RE tasks in natural language processing.

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