Results 151 to 160 of about 36,775 (295)
Meta-prompting Optimized Retrieval-augmented Generation
Retrieval-augmented generation resorts to content retrieved from external sources in order to leverage the performance of large language models in downstream tasks. The excessive volume of retrieved content, the possible dispersion of its parts, or their
Rodrigues, João, Branco, António
core
ABSTRACT Lung adenocarcinoma (LUAD) remains a leading cause of cancer mortality with limited therapeutic options. Disulfidptosis, a novel cell death modality driven by disulfide stress, represents a promising target, yet its regulation in LUAD is poorly defined. Here, we identify Pannexin 2 (PANX2) as a tumor suppressor in LUAD.
Yi Chen +7 more
wiley +1 more source
Retrieval-Augmented Generation for AI-Generated Content: A Survey
Advancements in model algorithms, the growth of foundational models, and access to high-quality datasets have propelled the evolution of Artificial Intelligence Generated Content (AIGC).
Penghao Zhao +9 more
doaj +1 more source
Retrieval Augmented Generation Optimizations [PDF]
This product-based thesis explores the optimization of Retrieval-Augmented Generation (RAG) systems, focusing on improving the precision and relevance of skill and occupation retrieval from CV texts. The study aims to enhance an existing Proof of Concept
Rolle, Robin
core
PLD3 activates the lysosomal‐AKT‐NF‐κB axis to drive cellular senescence in macrophages, establishing an immunosuppressive TME by limiting the infiltration of cytotoxic T, NK, and NKT cells, which confers resistance to anti‐PD‐1 therapy. Abrine inhibits PLD3 expression, restoring antitumor immunity and synergizing with anti‐PD‐1 treatment.
Xingtu Qin +11 more
wiley +1 more source
OptoChat: a large language model with retrieval augmented generation for optics
Large language models (LLMs) show strong performance in general text generation and knowledge-based question answering (QA). However, a substantial performance gap remains in optics, a knowledge-intensive scientific field.
Xiaoqing Bao +10 more
doaj +1 more source
Quels usages du “Retrieval-augmented generation” en SHS ?
Ce court billet introduit les Retrieval-augmented generation dans leurs usages au sein des displines SHS, en particulier la recherche documentaire et l'histoireCe court billet introduit les Retrieval-augmented generation dans leurs usages au sein des ...
Pouyllau, Stéphane
core
Multi-agent Retrieval-Augmented Generation for Enhancing Answer Generation and Knowledge Retrieval
289302Large language models (LLMs) have shown remarkable capabilities in natural language processing but often exhibit factual inconsistencies when applied to knowledge-intensive tasks, with hallucination rates as high as 30% in open-domain question ...
Jain, Bhavesh Mahender, Kumar, Deepak
core +1 more source
Optimizing the heterointerface coverage of the conducting Ni2P nanoparticles on the surface of BVO photoanode manipulates the surface Fermi‐level modulation, which significantly enhances photoelectrochemical oxygen evolution reaction. Our multiscale simulations and experimental results reveal that an optimal Ni2P coverage of 9.4% pins the surface Fermi
Phuong Thi Pham +14 more
wiley +1 more source
Clinical entity augmented retrieval for clinical information extraction
Large language models (LLMs) with retrieval-augmented generation (RAG) have improved information extraction over previous methods, yet their reliance on embeddings often leads to inefficient retrieval.
Ivan Lopez +11 more
doaj +1 more source

