用大模型自动构建企业级数据表，助力欧盟森林砍伐监管合规。
An Automated LLM-based Pipeline for Asset-Level Database Creation to Assess Deforestation Impact
- 基于指令+角色+零样本思维链的提示技术提升信息抽取精度。
- 在矿业、油气、公用事业领域实现高覆盖率验证，准确率显著优于传统方法。
- 适合关注可持续发展、碳排放披露与企业环境责任的从业者使用。
欧盟森林砍伐法规（EUDR）要求企业证明其产品不导致森林砍伐，亟需精确的资产级环境影响数据。现有数据库缺乏细节，过度依赖宽泛财务指标和人工采集，限制了监管合规与环境建模。本研究提出一种基于大模型的全自动端到端数据提取流程，专门针对高森林砍伐风险行业。该流程采用指令式、角色化、零样本思维链（IRZ-CoT）提示策略以提升数据抽取准确性，并引入检索增强验证（RAV）机制，通过实时网络搜索提高数据可靠性。在证券交易所（SEC EDGAR）文件中应用于矿业、油气与公用事业领域，结果表明该方法在数据提取准确性和验证覆盖范围上均显著优于传统零样本提示，推动NLP自动化在监管合规、企业社会责任（CSR）与ESG领域的应用，具备广泛行业适用性。
原文摘要 · Abstract (English)
The European Union Deforestation Regulation (EUDR) requires companies to prove their products do not contribute to deforestation, creating a critical demand for precise, asset-level environmental impact data. Current databases lack the necessary detail, relying heavily on broad financial metrics and manual data collection, which limits regulatory compliance and accurate environmental modeling. This study presents an automated, end-to-end data extraction pipeline that uses LLMs to create, clean, and validate structured databases, specifically targeting sectors with a high risk of deforestation. The pipeline introduces Instructional, Role-Based, Zero-Shot Chain-of-Thought (IRZ-CoT) prompting to enhance data extraction accuracy and a Retrieval-Augmented Validation (RAV) process that integrates real-time web searches for improved data reliability. Applied to SEC EDGAR filings in the Mining, Oil & Gas, and Utilities sectors, the pipeline demonstrates significant improvements over traditional zero-shot prompting approaches, particularly in extraction accuracy and validation coverage. This work advances NLP-driven automation for regulatory compliance, CSR (Corporate Social Responsibility), and ESG, with broad sectoral applicability.
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