Results 71 to 80 of about 3,227,651 (213)

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

Assessing the ability of large language models to summarize and generate maxillofacial prosthetic treatment options

Journal of Prosthodontics, EarlyView.
Abstract Purpose The aim was to evaluate the ability of four large language models (LLMs) (OpenAI's ChatGPT‐3.5, Microsoft 365 Copilot, DeepSeek‐R1, and Google Gemini 2.5 Pro) to develop treatment options when presented with clinical cases published in the maxillofacial prosthodontics literature.
Leila M. Sears, Merve Koseoglu, Stavroula Antonopoulou, Sarah Kay Youny Lee, Michael Kase, Amanda Colebeck, Yu‐Chun (Jessie) Lin, Heidi Huber, Maribeth Krzesinski, Alvin G. Wee, Cortino Sukotjo   +10 more
wiley   +1 more source

AI foundation models in plant biology

New Phytologist, EarlyView.
Foundation models decode genomes, engineer proteins, phenotype crops, and drive AI agents across plant biology. This figure was created in BioRender (BioRender.com/xcmmkel). Summary Rapid technological progress has enabled plant biologists to accumulate unprecedented volumes of multi‐scale, multi‐modal data, yet this abundance of data has intensified ...
Haopeng Yu
wiley   +1 more source

RAGdeterm: Deterministic retrieval-augmented generation for code generation

SoftwareX
Large language models (LLMs) are increasingly used in software development, yet effective code generation requires reliable access to up-to-date project-specific source code. This paper introduces RAGdeterm, a deterministic Retrieval-Augmented Generation
A. Bochenek, J. Protasiewicz, W. Pedrycz
doaj   +1 more source

Economic Growth Vulnerability Across Euro Area Countries

Oxford Bulletin of Economics and Statistics, EarlyView.
ABSTRACT We analyse growth vulnerability in the four largest Euro Area (EA) economies, measured as a lower quantile of the growth distribution conditional on EA‐wide and country‐specific macroeconomic/financial factors. Growth densities are obtained under a normal activity scenario and under stressed conditions.
Claudio Lissona, Esther Ruiz
wiley   +1 more source

DARE-RAG: Difficulty-Aware Retrieval Expansion for Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) systems face a fundamental trade-off: query expansion can improve retrieval effectiveness for ambiguous or underspecified queries, yet indiscriminate expansion introduces unnecessary latency and retrieval noise ...
Lixiang Zhu
core   +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

Benchmarking commercial large language models for gene–disease–phenotype extraction from full‐text human genetics literature

Quantitative Biology, Volume 14, Issue 4, December 2026.
Abstract Manual curation of gene–disease–phenotype relationships from the human genetics literature is a persistent bottleneck for maintaining its bioinformatics databases. Whereas large language models (LLMs) offer a promising alternative, there is currently no systematic benchmark that evaluates whether state‐of‐the‐art commercial LLMs can perform ...
Danqing Yin, Matthew Ka Siu Leung, Darren Wan Ho Pun, Fiona Haixin Chen, Julie Yujin Kwon, Xinyi Lin, Joshua W. K. Ho   +6 more
wiley   +1 more source

Retrieval-Augmented Generation (RAG) Systems for Knowledge Management

Artificial Intelligence (AI) and Large Language Models (LLMs) have significantly transformed knowledge management by enabling intelligent, context-aware, and automated information access.
Louis Pouzin, Jacques Arsac
core   +1 more source

DuetRAG: Collaborative Retrieval-Augmented Generation [PDF]

Retrieval-Augmented Generation (RAG) methods augment the input of Large Language Models (LLMs) with relevant retrieved passages, reducing factual errors in knowledge-intensive tasks.
Zhuang, Yueting, Zhang, Wenqiao, Tang, Siliang, Huang, Jingsheng, Jiao, Dian, Cai, Li   +5 more
core   +1 more source