Retrieval-Augmented Generation (RAG) has greatly improved large language models (LLMs) by enabling them to generate accurate, contextually grounded responses through the integration of external information. However, conventional RAG approaches, which prioritize top-ranked documents based solely on query-context relevance, often introduce redundancy and conflicting information. This issue is particularly evident in unsupervised retrieval settings, where there are no mechanisms to effectively mitigate these problems, leading to suboptimal context selection. To address this, we propose Selection using Matrices for Augmented Retrieval (SMART) in question answering tasks, a fully unsupervised and training-free framework designed to optimize context selection in RAG. SMART leverages Determinantal Point Processes (DPPs) to simultaneously model relevance, diversity and conflict, ensuring the selection of potentially high-quality contexts. Experimental results across multiple datasets demonstrate that SMART significantly enhances QA performance and surpasses previous unsupervised context selection methods, showing a promising strategy for RAG.
Smarter, not Bigger: Fine-Tuned RAG-Enhanced LLMs for Automotive HIL Testing
Communication Systems Group CSG, Department of Informatics IfI, University of Zurich UZH, 8050 Zürich, Switzerland（苏黎世大学信息学院通信系统组）；Volvo Car Corporation, 405 31 Göteborg, Sweden（沃尔沃汽车公司）
Self-Aware Vector Embeddings for Retrieval-Augmented Generation: A Neuroscience-Inspired Framework for Temporal, Confidence-Weighted, and Relational Knowledge
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；University of New Mexico（新墨西哥大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Maryland Baltimore County（马里兰大学巴尔的摩分校）；Advanced Research in Cyber Systems, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室网络系统高级研究部）；Theoretical Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室理论部）
Evaluating a Multi-Agent Voice-Enabled Smart Speaker for Care Homes: A Safety-Focused Framework
University of Hull, UK（赫尔大学）；University of Southampton, UK（南安普顿大学）；Connexin, Hull, UK（Connexin公司）；Leeds Teaching Hospital NHS Trust, UK（利兹教学医院国家健康服务信托）
Less is More: Lightweight Prompt Compression for Question Answering Applications on Edge Devices
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）
