面向水和废水管理的领域适应大型语言模型:方法、数据集和基准

IF 11.4 1区 工程技术 Q1 ENGINEERING, CHEMICAL
Boyan Xu, Guanlan Wu, Zihao Li, Guangming Xu, Huabin Zeng, Rui Tong, How Yong Ng
{"title":"面向水和废水管理的领域适应大型语言模型:方法、数据集和基准","authors":"Boyan Xu, Guanlan Wu, Zihao Li, Guangming Xu, Huabin Zeng, Rui Tong, How Yong Ng","doi":"10.1038/s41545-025-00509-8","DOIUrl":null,"url":null,"abstract":"<p>Large language models (LLMs) have shown significant promise for water and wastewater management. However, current foundation models are not yet reliable. This Perspective outlines a pathway for customizing foundation models into WaterGPTs (specialized LLMs for water and wastewater management). We present key methodologies for adapting foundation models into WaterGPTs, including prompt engineering, knowledge and tool augmentation, and fine-tuning, and they are illustrated through representative examples. Then, we highlight the importance of diverse and ethically sourced datasets to customize foundation models, and we propose strategies for efficiently extracting high-quality information to customize foundation models. Further, we advocate for the development of a secure, informative, and dynamic evaluation benchmark that will guide the creation of more reliable WaterGPT. To illustrate practical LLM deployment in water sectors, we envision a specialized WaterGPT in wastewater treatment plants, which could integrate specific biological/chemical knowledge and advanced tools to manage intricate processes of activated sludge. Collectively, we aim to lower barriers for non-AI water-domain-specific experts and bridge the gap between experimental and computational research in water and wastewater management.</p>","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":"11 1","pages":""},"PeriodicalIF":11.4000,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Towards domain-adapted large language models for water and wastewater management: methods, datasets and benchmarking\",\"authors\":\"Boyan Xu, Guanlan Wu, Zihao Li, Guangming Xu, Huabin Zeng, Rui Tong, How Yong Ng\",\"doi\":\"10.1038/s41545-025-00509-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Large language models (LLMs) have shown significant promise for water and wastewater management. However, current foundation models are not yet reliable. This Perspective outlines a pathway for customizing foundation models into WaterGPTs (specialized LLMs for water and wastewater management). We present key methodologies for adapting foundation models into WaterGPTs, including prompt engineering, knowledge and tool augmentation, and fine-tuning, and they are illustrated through representative examples. Then, we highlight the importance of diverse and ethically sourced datasets to customize foundation models, and we propose strategies for efficiently extracting high-quality information to customize foundation models. Further, we advocate for the development of a secure, informative, and dynamic evaluation benchmark that will guide the creation of more reliable WaterGPT. To illustrate practical LLM deployment in water sectors, we envision a specialized WaterGPT in wastewater treatment plants, which could integrate specific biological/chemical knowledge and advanced tools to manage intricate processes of activated sludge. Collectively, we aim to lower barriers for non-AI water-domain-specific experts and bridge the gap between experimental and computational research in water and wastewater management.</p>\",\"PeriodicalId\":19375,\"journal\":{\"name\":\"npj Clean Water\",\"volume\":\"11 1\",\"pages\":\"\"},\"PeriodicalIF\":11.4000,\"publicationDate\":\"2025-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"npj Clean Water\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1038/s41545-025-00509-8\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Clean Water","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1038/s41545-025-00509-8","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0

摘要

大型语言模型(llm)在水和废水管理方面显示出了巨大的前景。然而,目前的基础模型尚不可靠。本展望概述了将基础模型定制为WaterGPTs(专门用于水和废水管理的法学硕士)的途径。我们提出了将基础模型适应为WaterGPTs的关键方法,包括快速工程、知识和工具增强以及微调,并通过代表性示例进行了说明。然后,我们强调了多样化和道德来源的数据集对基础模型定制的重要性,并提出了有效提取高质量信息以定制基础模型的策略。此外,我们提倡开发一种安全、信息丰富和动态的评估基准,以指导创建更可靠的水力发电系统。为了说明LLM在水行业的实际应用,我们设想在废水处理厂建立一个专门的waterergpt,它可以整合特定的生物/化学知识和先进的工具来管理复杂的活性污泥过程。总的来说,我们的目标是降低非人工智能水领域专家的门槛,弥合水和废水管理实验研究和计算研究之间的差距。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Towards domain-adapted large language models for water and wastewater management: methods, datasets and benchmarking

Towards domain-adapted large language models for water and wastewater management: methods, datasets and benchmarking

Large language models (LLMs) have shown significant promise for water and wastewater management. However, current foundation models are not yet reliable. This Perspective outlines a pathway for customizing foundation models into WaterGPTs (specialized LLMs for water and wastewater management). We present key methodologies for adapting foundation models into WaterGPTs, including prompt engineering, knowledge and tool augmentation, and fine-tuning, and they are illustrated through representative examples. Then, we highlight the importance of diverse and ethically sourced datasets to customize foundation models, and we propose strategies for efficiently extracting high-quality information to customize foundation models. Further, we advocate for the development of a secure, informative, and dynamic evaluation benchmark that will guide the creation of more reliable WaterGPT. To illustrate practical LLM deployment in water sectors, we envision a specialized WaterGPT in wastewater treatment plants, which could integrate specific biological/chemical knowledge and advanced tools to manage intricate processes of activated sludge. Collectively, we aim to lower barriers for non-AI water-domain-specific experts and bridge the gap between experimental and computational research in water and wastewater management.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
npj Clean Water
npj Clean Water Environmental Science-Water Science and Technology
CiteScore
15.30
自引率
2.60%
发文量
61
审稿时长
5 weeks
期刊介绍: npj Clean Water publishes high-quality papers that report cutting-edge science, technology, applications, policies, and societal issues contributing to a more sustainable supply of clean water. The journal's publications may also support and accelerate the achievement of Sustainable Development Goal 6, which focuses on clean water and sanitation.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信