Results 11 to 20 of about 3,227,651 (213)

EMasjid, Islamic Chatbot Application With RAG (RetrievalAugmented Generation)

Informatics, Electrical, and Electronics Engineering
The development of a religious chatbot is crucial for fostering good understanding and mitigating potential social conflicts arising from religious misunderstandings.
Kholid Fathoni, Aris Sudaryanto, Iwan Syarif, Zakha Maisat Eka Darmawan, Andhik Ampuh Yunanto, Prasetyo, Rendra Suprobo Aji, Aryazaky Iman Fauzy, Rizky Yuniar Hakkun   +8 more
doaj   +2 more sources

Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering

, 2023
Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia-based external knowledge base and is not optimized for use in other specialized domains such as ...
Suranga Nanayakkara, Rana, Rajib, Shamane Siriwardhana, Elliott Wen, Tharindu Kaluarachchi, Wen, Elliott, Nanayakkara, Suranga, Rivindu Weerasekera, Kaluarachchi, Tharindu, Weerasekera, Rivindu, Rajib Rana, Siriwardhana, Shamane   +11 more
core   +1 more source

Hallucination Reduction in Retrieval-Augmented Generation (RAG)

Research Status: Ongoing (Work in Progress) This research project focuses on developing novel methodologies to reduce hallucinations in Retrieval-Augmented Generation (RAG) systems using Large Language Models (LLMs).
Jahan Zaib
core   +6 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

MEGA-RAG: a retrieval-augmented generation framework with multi-evidence guided answer refinement for mitigating hallucinations of LLMs in public health

Frontiers in Public Health
IntroductionThe increasing adoption of large language models (LLMs) in public health has raised significant concerns about hallucinations-factually inaccurate or misleading outputs that can compromise clinical communication and policy decisions.MethodsWe
Shan Xu, Shan Xu, Zhaokun Yan, Chengxiao Dai, Chengxiao Dai, Fan Wu   +5 more
doaj   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Context-Aware Retrieval Augmented Generation Using Similarity Validation to Handle Context Inconsistencies in Large Language Models

IEEE Access
Large Language Models (LLMs) have transformed natural language processing by offering human-like responses. However, issues such as incorrect information (hallucinations) and errors in specific subject areas remain, especially in Retrieval Augmented ...
Enrico Collini, Felix Indra Kurniadi, Paolo Nesi, Gianni Pantaleo   +3 more
doaj   +1 more source

Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design

Advanced Science, EarlyView.
A large language model (LLM) based pipeline is developed to automatically extract a comprehensive and accurate multicomponent alloy database from literature corpus. The extracted dataset is integrated with sustainability indicators to identify potential alloys that outperform existing industrial benchmark materials in terms of both performance and ...
Aravindan Kamatchi Sundaram, Mohit Chakraborty, Sai Mani Kumar Devathi, B. Pabitramohan Prusty, Rohit Batra   +4 more
wiley   +1 more source

Closing the Empirical Loop: Autonomous AI Agents Conduct End‐to‐end Research With Human Participants

Advanced Science, EarlyView.
A multi‐agent AI system autonomously executes the complete scientific workflow, from hypothesis to manuscript, across three psychological studies involving 288 participants. The system designs experiments, collects real world data, develops analysis pipelines, and writes manuscripts with theoretical rigor comparable to experienced researchers.
Gabrielle Wehr, Reuben Rideaux, Amaya J. Fox, David R. Lightfoot, Jason Tangen, Jason B. Mattingley, Shane E. Ehrhardt   +6 more
wiley   +1 more source

Retrieval-augmented AI assistants for healthcare: System design and evaluation [PDF]

This dissertation explores the application of Retrieval-Augmented Generation (RAG) in AI models to address the challenges posed by fragmented personal health data.
Meleka, Mark
core   +1 more source