A Comprehensive Survey of Retrieval-Augmented Large Language Models for Decision Making in Agriculture: Unsolved Problems and Research Opportunities DOI Open Access
Artem Vizniuk, Grygorii Diachenko, Іvan Laktionov

et al.

Journal of Artificial Intelligence and Soft Computing Research, Journal Year: 2024, Volume and Issue: 15(2), P. 115 - 146

Published: Dec. 1, 2024

Abstract The breakthrough in developing large language models (LLMs) over the past few years has led to their widespread implementation various areas of industry, business, and agriculture. aim this article is critically analyse generalise known results research directions on approaches development utilisation LLMs, with a particular focus functional characteristics when integrated into decision support systems (DSSs) for agricultural monitoring. subject integration LLMs DSSs agrotechnical main scientific applied are as follows: world experience using improve processes been analysed; critical analysis carried out, application architectures have identified; necessity focusing retrieval-augmented generation (RAG) an approach solving one limitations which limited knowledge base training data, established; prospects agriculture analysed highlight trustworthiness, explainability bias reduction priority research; potential socio-economic effect from RAG sector substantiated.

Language: Английский

Large language models for building energy applications: Opportunities and challenges DOI Creative Commons
Mingzhe Liu, Yadong Zhang, Jianli Chen

et al.

Building Simulation, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 17, 2025

Language: Английский

Citations

2
Domain-specific large language models for fault diagnosis of heating, ventilation, and air conditioning systems by labeled-data-supervised fine-tuning DOI Creative Commons
Jian Zhang, Chaobo Zhang,

Jie Lu

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 377, P. 124378 - 124378

Published: Sept. 5, 2024

Language: Английский

Citations

8
Exploring automated energy optimization with unstructured building data: A multi-agent based framework leveraging large language models DOI
Tong Xiao, Peng Xu

Energy and Buildings, Journal Year: 2024, Volume and Issue: 322, P. 114691 - 114691

Published: Aug. 23, 2024

Language: Английский

Citations

6
Defining and Generating Operation and Maintenance Management Requirements in Digital Twin Applications Using the DT-GPT Framework DOI
Sheng Bao,

Hangdong Bu

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112356 - 112356

Published: March 1, 2025

Language: Английский

Citations

0
Building a construction law knowledge repository to enhance general-purpose large language model performance on domain question-answering: a case of China DOI
Shenghua Zhou, Hongyu Wang, S. Thomas Ng

et al.

Engineering Construction & Architectural Management, Journal Year: 2025, Volume and Issue: unknown

Published: April 30, 2025

Purpose Achieving smart question-answering (QA) for construction laws (CLs) holds significant promise in aiding domain professionals with legal inquiries. Existing studies of law (CLQA) rely on learning-based models, which require extensive training data and are limited to a narrow QA scope. Meanwhile, general-purpose large language models (GPLLMs) possess great potential CLQA but fall short domain-specific knowledge. This study aims propose data-driven expertise-based approach develop knowledge repository (CLKR) validate its effectiveness enhancing the performance GPLLMs. Design/methodology/approach methodology includes (1) recognizing 702 candidate CL documents from 374,992 official judgments, (2) building CLKR 387 filtered covering eight areas, (3) integrating seven representative GPLLMs (4) constructing 2,140-question dataset Professional Construction Engineer Qualification Examinations (PCEQEs) during 2014–2023 compare between pairs without CLKR. Findings The significantly enhances GPLLMs, yielding an impressive average accuracy increase 21.1%, individual improvements ranging 9.9 44.9%. Furthermore, boosts single-answer questions by 14.9% multiple-answer 38.3%. Additionally, enhancements across 8 areas 14.5 28.2%. Originality/value proposes developing external base empower expanding scope while bypassing complex traditional models. Moreover, this confirms augmenting GPLLM offers reusable test as benchmark.

Language: Английский

Citations

0
Self-attention variational autoencoder-based method for incomplete model parameter imputation of digital twin building energy systems DOI

Jie Lu,

Chaobo Zhang, Bozheng Li

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: unknown, P. 115162 - 115162

Published: Dec. 1, 2024

Language: Английский

Citations

0
Deep generative models in energy system applications: Review, challenges, and future directions DOI
Xiangyu Zhang, Andrew Glaws, Alexandre Cortiella

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 380, P. 125059 - 125059

Published: Dec. 13, 2024

Language: Английский

Citations

0
A Comprehensive Survey of Retrieval-Augmented Large Language Models for Decision Making in Agriculture: Unsolved Problems and Research Opportunities DOI Open Access
Artem Vizniuk, Grygorii Diachenko, Іvan Laktionov

et al.

Journal of Artificial Intelligence and Soft Computing Research, Journal Year: 2024, Volume and Issue: 15(2), P. 115 - 146

Published: Dec. 1, 2024

Abstract The breakthrough in developing large language models (LLMs) over the past few years has led to their widespread implementation various areas of industry, business, and agriculture. aim this article is critically analyse generalise known results research directions on approaches development utilisation LLMs, with a particular focus functional characteristics when integrated into decision support systems (DSSs) for agricultural monitoring. subject integration LLMs DSSs agrotechnical main scientific applied are as follows: world experience using improve processes been analysed; critical analysis carried out, application architectures have identified; necessity focusing retrieval-augmented generation (RAG) an approach solving one limitations which limited knowledge base training data, established; prospects agriculture analysed highlight trustworthiness, explainability bias reduction priority research; potential socio-economic effect from RAG sector substantiated.

Language: Английский

Citations

0