Results 41 to 50 of about 36,775 (295)

Implementing Retrieval-Augmented Generation for Academic Libraries

International Journal of Librarianship
This article details the technical development of a Retrieval-Augmented Generation (RAG) system designed to enhance discovery within an academic library's institutional repository.
Wei Xuan
doaj   +1 more source

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

Data Imputation Based on Retrieval-Augmented Generation

Applied Sciences
Modern organizations collect increasing volumes of data to drive decision-making, often stored in centralized repositories such as data lakes, which consist of diverse structured and unstructured datasets.
Xiaojun Shi, Jiacheng Wang, Gregorius Justin Chung, Derick Julian, Lianpeng Qiao   +4 more
doaj   +1 more source

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

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

Hybrid retrieval generation for structured reasoning with large language models

Discover Artificial Intelligence
Large Language Models (LLMs) exhibit strong generative capabilities but remain limited in structured knowledge domains due to factual inconsistency, shallow multi-hop reasoning, and weak alignment with domain constraints.
Rathinasamy Muthusami, Kandhasamy Saritha   +1 more
doaj   +1 more source

Resource-Constrained Evaluation of Quantized Local LLMs for Retrieval-Augmented Generation on a Reduced-Corpus Setting

IEEE Access
This paper studies retrieval-augmented generation (RAG) under a realistic local deployment constraint. Rather than proposing a new retriever or generator architecture, the paper evaluates how local, quantized RAG behaves when answer quality, provenance ...
Marcio L. Lima de Oliveira, Jes de Jesus Fiais Cerqueira   +1 more
doaj   +1 more source

Cross‐Scale Hierarchical Targeted Delivery System Based on Small‐Scale Magnetic Robots

Advanced Robotics Research, EarlyView.
This article reviews a cross‐scale hierarchical targeted delivery system that integrates magnetic continuum robots and magnetic microrobots. By combining rapid long‐range navigation with precise microscale targeting, the system overcomes key limitations of single‐scale approaches.
Junjian Zhou, Zhiqiang Chen, Yuanbiao Ma, Zhiqiang Tang, Qianqian Wang   +4 more
wiley   +1 more source

Improving negative rejection ability in language models: A review of fine-tuned LLMs, RAG, and RAFT

Journal of King Saud University: Computer and Information Sciences
Large Language Models (LLMs) excel in text understanding and generation but struggle to reject irrelevant, ambiguous, or misleading queries, termed negative rejection, impacting reliability in high-stakes contexts.
Li Bowen, Zhong Zhuoqing, Zhang Hengyu, Sun Xubin, Baha Ihnaini   +4 more
doaj   +1 more source

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