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Retrieval-Augmented Detection of Potentially Abusive Clauses in Chilean Terms of Service

A study on automated detection and classification of potentially abusive clauses in Chilean Terms of Service using a retrieval-augmented generation framewo

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Retrieval-Augmented Detection of Potentially Abusive Clauses in Chilean Terms of Service

By Christoffer Loeffler, Tomás Rey Pizarro, Daniel Ignacio Miranda Vásquez, Andrea Martínez FreilearXiv
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The paper presents a framework for detecting and classifying potentially abusive clauses in Chilean Terms of Service. It combines efficient clause detection, hybrid dense-sparse retrieval, reranking, and prompt augmentation to support medium-sized open-weight language models.

The study also contributes a refined legal annotation scheme and a practical design for AI-assisted consumer contract review.

Abstract

The paper presents a framework for detecting and classifying potentially abusive clauses in Chilean Terms of Service. It combines efficient clause detection, hybrid dense-sparse retrieval, reranking, and prompt augmentation to support medium-sized open-weight language models. The study also contributes a refined legal annotation scheme and a practical design for AI-assisted consumer contract review.

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abusive clausesterms of serviceclause detectionlanguage modelslegal annotationKnowledge GraphsStructured ContentContent EngineeringLarge Language Models
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