Results 51 to 60 of about 16,728 (252)

Bridging the Question–Answer Gap in Retrieval-Augmented Generation: Hypothetical Prompt Embeddings

IEEE Access
Retrieval-Augmented Generation (RAG) systems synergize retrieval mechanisms with generative language models to enhance the accuracy and relevance of responses. However, bridging the style gap between user queries and relevant information in document text
Domen Vake, Jernej Vicic, Aleksandar Tosic   +2 more
doaj   +1 more source

Source Attribution in Retrieval-Augmented Generation

While attribution methods, such as Shapley values, are widely used to explain the importance of features or training data in traditional machine learning, their application to Large Language Models (LLMs), particularly within Retrieval-Augmented Generation (RAG) systems, is nascent and challenging.
Ikhtiyor Nematov, Tarik Kalai, Elizaveta Kuzmenko, Gabriele Fugagnoli, Dimitris Sacharidis, Katja Hose, Tomer Sagi   +6 more
openaire   +4 more sources

Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space

Advanced Robotics Research, EarlyView.
A quadruped robot masters dynamic jumps through constrained spaces with animal‐inspired moves and intelligent vision control. This hierarchical learning approach combines imitation of biological agility with real‐time trajectory planning. Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating ...
Zeren Luo, Jiahui Zhang, Yimin Han, Ji Ma, Minghao Lu, Ioannis Havoutis, Peng Lu   +6 more
wiley   +1 more source

Multimodal retrieval-augmented generation framework for visually rich knowledge in the architecture domain

Architectural Intelligence
Architectural design relies heavily on rich and multimodal knowledge—including text descriptions, detailed tables, and complex visual information—to inform creative and technical decision-making.
Xianchuan Meng, Ziyu Tong
doaj   +1 more source

Evaluation of Retrieval-Augmented Generation: A Survey

Retrieval-Augmented Generation (RAG) has recently gained traction in natural language processing. Numerous studies and real-world applications are leveraging its ability to enhance generative models through external information retrieval. Evaluating these RAG systems, however, poses unique challenges due to their hybrid structure and reliance on ...
Hao Yu 0030, Aoran Gan, Kai Zhang 0038, Shiwei Tong, Qi Liu 0003, Zhaofeng Liu   +5 more
openaire   +2 more sources

The Future of Research in Cognitive Robotics: Foundation Models or Developmental Cognitive Models?

Advanced Robotics Research, EarlyView.
Research in cognitive robotics founded on principles of developmental psychology and enactive cognitive science would yield what we seek in autonomous robots: the ability to perceive its environment, learn from experience, anticipate the outcome of events, act to pursue goals, and adapt to changing circumstances without resorting to training with ...
David Vernon
wiley   +1 more source

LLM based QA chatbot builder: A generative AI-based chatbot builder for question answering

SoftwareX
Large language model (LLM) based interactive chatbots have been gaining popularity as a tool to serve organizational information among people. Building such a tool goes through several development phases i.e.
Md. Shahidul Salim, Sk Imran Hossain, Tanim Jalal, Dhiman Kumer Bose, Mohammad Jahid Ibna Basher   +4 more
doaj   +1 more source

Retrieval-Augmented Generation with Graphs (GraphRAG)

CoRR
Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information, such as knowledge, skills, and tools from external sources. Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information, making it a golden resource for RAG in
Haoyu Han 0001, Yu Wang 0160, Harry Shomer, Kai Guo 0003, Jiayuan Ding, Yongjia Lei, Mahantesh Halappanavar, Ryan A. Rossi, Subhabrata Mukherjee, Xianfeng Tang, Qi He 0002, Zhigang Hua, Bo Long, Tong Zhao 0003, Neil Shah, Amin Javari, Yinglong Xia, Jiliang Tang   +17 more
openaire   +3 more sources

Improving the Robustness of Visual Teach‐and‐Repeat Navigation Using Drift Error Correction and Event‐Based Vision for Low‐Light Environments

Advanced Robotics Research, EarlyView.
Visual teach‐and‐repeat (VTR) navigation allows robots to learn and follow routes without building a full metric map. We show that navigation accuracy for VTR can be improved by integrating a topological map with error‐drift correction based on stereo vision.
Fuhai Ling, Ze Huang, Tony J. Prescott
wiley   +1 more source

Retrieval-Augmented Generation with Hierarchical Knowledge

Findings of the Association for Computational Linguistics: EMNLP 2025
EMNLP 2025 ...
Haoyu Huang, Yongfeng Huang, Junjie Yang, Zhenyu Pan, Yongqiang Chen 0002, Kaili Ma 0001, Hongzhi Chen, James Cheng   +7 more
openaire   +3 more sources