整合微调和检索增强生成以应对污染控制指南的应用挑战
Zhuhan Zhou1, Tingting Jiang1, Qiang Jin2
1School of Economics and Management, Shanghai Institute of Technology, Shanghai, 200235, China.
Journal of environmental management
|February 3, 2026
概括
这项研究将检索增强生成 (RAG) 与微调 (FT) 结合起来,以简化复杂的环境污染控制指南. 混合模型增强了文本解释和生成,以更好地理解和实施政策.
科学领域:
- 环境科学 环境科学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 环境污染控制指南复杂,公众和机构难以理解.
- 这种复杂性阻碍了关键环境政策的有效实施.
研究的目的:
- 系统地应用和评估混合检索-增强生成 (RAG) 和微调 (FT) 模型来解释和生成环境污染控制文本.
- 提高环境技术指导的理解性和可访问性.
主要方法:
- 开发了一个混合系统,将RAG和FT技术结合起来.
- 在当地环境机构的文件上对该系统进行了评估.
- 使用自动化指标 (ROUGE,BLEU,BERTScore) 和专家评估进行评估.
主要成果:
- 混合RAG-FT模型在自动化指标和专家评估方面都超过了其他模型.
- 该模型展示了能够连贯地解释和生成技术污染控制文本的能力.
- 获得了更高的分数,这表明在文本解释和生成方面表现有所改善.
结论:
- 混合RAG-FT模型显示了提高环境政策的理解和应用的巨大潜力.
- 这种方法可以支持更可靠的政策实施,并推进数据驱动的环境治理.
- 促进对环境法规的智能解释,以改善管理.
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