Results 61 to 70 of about 3,227,651 (213)

The State of the Art in Visualization Literacy

Computer Graphics Forum, EarlyView.
In this survey paper, we review 385 visualization literacy papers to understand the state of the field. We discuss 5 different types of research contributions, as well as 4 competency themes that capture the skills relevant to visualization literacy. Abstract Research in visualization literacy explores the skills required to engage with visualizations.
Matthew Varona, Karen Bonilla, Maryam Hedayati, Alark Joshi, Matthew Kay, Lane Harrison, Carolina Nobre   +6 more
wiley   +1 more source

Harnessing Retrieval-Augmented Generation (RAG) for Uncovering Knowledge Gaps

, 2023
The paper presents a methodology for uncovering knowledge gaps on the internet using the Retrieval Augmented Generation (RAG) model. By simulating user search behaviour, the RAG system identifies and addresses gaps in information retrieval systems.
Hurtado, Joan Figuerola
core  

C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models [PDF]

Despite the impressive capabilities of large language models (LLMs) across diverse applications, they still suffer from trustworthiness issues, such as hallucinations and misalignments.
Li, Bo, Gürel, Nezihe Merve, Yu, Ning, Song, Dawn, Kang, Mintong   +4 more
core   +1 more source

GraphTrace: A Modular Retrieval Framework Combining Knowledge Graphs and Large Language Models for Multi-Hop Question Answering

Computers
This paper introduces GraphTrace, a novel retrieval framework that integrates a domain-specific knowledge graph (KG) with a large language model (LLM) to improve information retrieval for complex, multi-hop queries.
Anna Osipjan, Hanieh Khorashadizadeh, Akasha-Leonie Kessel, Sven Groppe, Jinghua Groppe   +4 more
doaj   +1 more source

Survey on Compositional 3D Indoor Scene Generation

Computer Graphics Forum, EarlyView.
This survey provides a comprehensive overview of compositional 3D indoor scene generation, introducing a unified framework for categorizing existing methods, comparing their strengths and limitations and identifying key challenges and future research directions.
H. I. I. Tam, H. I. D. Pun, A. T. Wang, X. Sun, Q. Wu, H. Lee, A. X. Chang, M. Savva   +7 more
wiley   +1 more source

MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation [PDF]

Large Language Models (LLMs) are becoming essential tools for various natural language processing tasks but often suffer from generating outdated or incorrect information.
Zheng, Yan, Das, Mahashweta, Wang, Guanchu, Hu, Mingzhi, Pan, Menghai, Rakesh, Vineeth, Zou, Na, Chang, Chia-Yuan, Yeh, Chin-Chia Michael, Xu, Zhichao, Jiang, Zhimeng   +10 more
core   +1 more source

Tabular Data Classification and Regression: XGBoost or Deep Learning With Retrieval-Augmented Generation

IEEE Access
Tabular data is the most prevalent form of structured data, necessitating robust models for classification and regression tasks. Traditional models like eXtreme Gradient Boosting (XGBoost) have gained popularity for their strong performance, while deep ...
Jonindo Pasaribu, Novanto Yudistira, Wayan Firdaus Mahmudy   +2 more
doaj   +1 more source

Emerging applications of large language models in ecology and conservation science

Conservation Biology, EarlyView.
Abstract Large language models (LLMs) mark a major development in artificial intelligence, with potentially transformative implications for ecology and conservation science. Built on advanced deep‐learning architectures, these models can support a wide range of tasks. We reviewed emerging applications of LLMs, drawing on the wider scientific literature
Christos Mammides, Hao Gu, Thilina S. Nimalrathna, Naufal Rahman Avicena, Harris Papadopoulos, Ahimsa Campos‐Arceiz   +5 more
wiley   +1 more source

Optimizing RAG in Programming Education: A Comparative Study of Models and Strategies

IEEE Access
This study examines how Retrieval-Augmented Generation (RAG) enhances the effectiveness of generative artificial intelligence in programming education.
Christopher C. Y. Yang, Guan-Jie Liu, Cheng-Jui Yu   +2 more
doaj   +1 more source

Role of Large Language Model—Artificial Intelligence in Enhancing Communication Skills in Undergraduate Oral Healthcare Students

European Journal of Dental Education, EarlyView.
ABSTRACT Introduction Artificial Intelligence (AI) is transforming dental education (DE) by advancing teaching strategies, clinical training, and patient care. Its integration allows for personalized learning experiences and realistic simulations, and equips students with the competencies required to deliver high‐quality oral healthcare in a digital ...
Munazza Khanzada, Panpan Zhang, Vu Anh Tran, Melanie Nasseripour, Ana Angelova, Avijit Banerjee, Alfie Abdul Rahman   +6 more
wiley   +1 more source