吴语-环境执法基准：评估大语言模型在环境执法中的基准
WuYu-EnvLE-Bench: A Benchmark for Evaluating Large Language Models in Environmental Law Enforcement
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中文总结 AI 辅助
该研究针对大语言模型在环境执法中生成可追溯决策能力不明的问题，构建吴语-环境执法基准，含多任务多子领域实例，用AES和IEI评估模型，发现其在部分任务表现不佳，强调证据与规则感知的执法推理需求。
中文摘要 AI 辅助
大语言模型（LLMs）在环境执法中的应用日益受到关注，但其生成可追溯执法决策的能力尚不明晰。我们引入了吴语-环境执法基准（WuYu-EnvLE-Bench），它基于实际执法案例、监管标准和专家评审构建。该基准包含2521个基准实例、14项任务和12个跨执法前、执法中和执法后工作流程的污染介质子领域。我们使用绝对环境执法得分（AES）和智能执法指数（IEI）评估了开源和闭源大语言模型的能力、响应质量和资源效率。结果表明，大语言模型在规则受限任务上表现良好，但在证据链构建、矛盾检测、多源整合和程序判断方面仍不可靠。模型扩展也显示出收益递减：中型模型在结构化任务中接近领先模型，而大型模型无法可靠地克服证据推理瓶颈。吴语-环境执法基准强调了基于证据、规则感知和任务自适应执法推理的必要性。
英文摘要
Large language models (LLMs) are increasingly considered for environmental enforcement, but their ability to produce traceable enforcement decisions remains unclear. We introduce WuYu-EnvLE-Bench, a benchmark built from real enforcement cases, regulatory standards, and expert review. It contains 2,521 benchmark instances, 14 tasks, and 12 pollution-medium subdomains across pre-enforcement, in-enforcement, and post-enforcement workflows. Using Absolute Environmental Enforcement Score (AES) and Intelligent Enforcement Index (IEI), we evaluate open-source and closed-source LLMs across capability, response quality, and resource efficiency. Results show that LLMs perform well on rule-bounded tasks but remain unreliable in evidence-chain construction, contradiction detection, multi-source integration, and procedural judgment. Model scaling also shows diminishing returns: medium-sized models approach leading models in structured tasks, while larger models do not reliably overcome evidence-reasoning bottlenecks. WuYu-EnvLE-Bench highlights the need for evidence-grounded, rule-aware, and task-adaptive enforcement reasoning.
发表机构
- School of Environment, Tsinghua University（清华大学环境学院）
- College of Economics and Management, Beijing University of Technology（北京工业大学经济与管理学院）
- State Key Laboratory of Iron and Steel Industry Environmental Protection, School of Environment, Tsinghua University（清华大学环境学院钢铁工业环境保护国家重点实验室）
- Appraisal Center for Environmental Engineering, Ministry of Ecology and Environment（生态环境部环境工程评估中心）
机构由 AI 辅助整理，请以论文原文为准。
