用AI拆解复杂服务条款，让用户看清数字权利边界。
Terminators: Terms of Service Parsing and Auditing Agents
- 分三步解析条款：提取关键项、验证内容真伪、规划责任应对
- 在OpenAI条款上测试，有效减少AI幻觉并提升可审计性
- 适合监管机构、公民团体做自动化政策审查
服务条款通常冗长且使用复杂的法律语言，使普通用户难以阅读和理解。为解决此问题，我们提出Terminators——一个模块化智能体框架，利用大语言模型（LLM）解析和审计服务条款。不同于将条款理解视为黑箱摘要任务，Terminators将任务分解为三个可解释的步骤：条款提取、内容验证与责任规划。我们在OpenAI服务条款上使用GPT-4o进行了验证，展示了降低幻觉、提升可审计性的策略。结果表明，结构化的基于智能体的LLM工作流能增强复杂法律文件的可用性与可执行性。通过将模糊条款转化为可操作、可验证的组成部分，Terminators推动了网络内容的伦理使用，提升了透明度，帮助用户理解数字权利，并支持监管或公众监督下的自动化政策审计。
原文摘要 · Abstract (English)
Terms of Service (ToS) documents are often lengthy and written in complex legal language, making them difficult for users to read and understand. To address this challenge, we propose Terminators, a modular agentic framework that leverages large language models (LLMs) to parse and audit ToS documents. Rather than treating ToS understanding as a black-box summarization problem, Terminators breaks the task down to three interpretable steps: term extraction, verification, and accountability planning. We demonstrate the effectiveness of our method on the OpenAI ToS using GPT-4o, highlighting strategies to minimize hallucinations and maximize auditability. Our results suggest that structured, agent-based LLM workflows can enhance both the usability and enforceability of complex legal documents. By translating opaque terms into actionable, verifiable components, Terminators promotes ethical use of web content by enabling greater transparency, empowering users to understand their digital rights, and supporting automated policy audits for regulatory or civic oversight.
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