mlsanityData to 14 Oct 2026

Terminators: Terms of Service Parsing and Auditing Agents

arXiv 2505.11672 ↗First posted 16 May 2025cs.IRcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 8 of its 32 references that are in the map. First posted on arXiv 16 May 2025.

Builds on

8 of 32 references.

References · 8

  1. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  2. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  3. 2023Benchmarking Large Language Models for News Summarizationcited by 445
  4. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 332
  5. 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 246
  6. 2023On Learning to Summarize with Large Language Models as Referencescited by 80
  7. 2018CLAUDETTE: an Automated Detector of Potentially Unfair Clauses in Online Terms of Servicecited by 73
  8. 2024Are LLM-based methods good enough for detecting unfair terms of service?cited by 1

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  3. 2026Provably Auditable and Safe LLM Agents from Human-Authored Ontologies
  4. 2024LAW: Legal Agentic Workflows for Custody and Fund Services Contracts
  5. 2025When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance
  6. 2026LawCompass: Navigating from Legal QA to Multi-Agent Deep Research with Grounded Evidence
  7. 2026LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI
  8. 2024LegalAgentBench: Evaluating LLM Agents in Legal Domain