Retrieval-augmented generation - Open Access .click

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
ACCEPT on IEEE/CVF Winter Conference on Applications of Computer Vision (WACV ...
LIU, Guoshan +5 more
openaire +2 more sources

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer +2 more
wiley +1 more source

Artificial intelligence-driven clinical guideline recommendations in maternal care: How trustworthy are they?

Biomédica: revista del Instituto Nacional de Salud
Introduction. Medical staff often face difficulties in consulting and applying clinical guidelines in practice. Large language models, especially when combined with retrieval-augmented generation, may help overcome these challenges by producing context ...
Jairo J. Pérez +9 more
doaj +1 more source

Chain-of-Retrieval Augmented Generation

CoRR
Accepted by NeurIPS ...
Liang Wang 0046 +5 more
openaire +3 more sources

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch +3 more
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

Ontology‐Aligned Structuring and Reuse of Multimodal Materials Data and Workflows Toward Automatic Reproduction

Advanced Engineering Materials, EarlyView.
Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari +5 more
wiley +1 more source

A retrieval-augmented large language model for agricultural advisory on crop varieties and cultivation techniques

CTU Journal of Innovation and Sustainable Development
This study presents the design and implementation of an agricultural advisory chatbot to help farmers access reliable information on crop varieties and cultivation techniques, with a focus on rice and mango.
Thanh Dien Tran +5 more
doaj +1 more source

Neurosymbolic Retrievers for Retrieval-Augmented Generation

IEEE Intelligent Systems
Retrieval Augmented Generation (RAG) has made significant strides in overcoming key limitations of large language models, such as hallucination, lack of contextual grounding, and issues with transparency. However, traditional RAG systems consist of three interconnected neural components - the retriever, re-ranker, and generator - whose internal ...
Yash Saxena, Manas Gaur
openaire +3 more sources

DigiChrom: A Domain Ontology for Semantic Representation of Trivalent Chromium Platings and Its Large Language Model‐Based Alignment With Multiple Mid‐Level Ontologies

Advanced Engineering Materials, EarlyView.
Digitalizing electroplating requires both domain knowledge and interoperability. This work introduces PlatOn, a domain ontology for trivalent chromium plating and coating characterization, and a hybrid pipeline that aligns it to a mid‐level reference ontology by combining eight similarity metrics with language model reasoning. Expert‐validated mappings
Janik Harter +10 more
wiley +1 more source

large language models
rag
retrieval augmented generation

artificial intelligence
llm
retrieval-augmented generation rag

information retrieval