LegalWebAgent: Empowering Access to Justice via LLM-Based Web Agents
Jinzhe Tan, Karim Benyekhlef

TL;DR
LegalWebAgent leverages multimodal large language models to create an autonomous web agent that assists users in navigating legal websites, understanding legal information, and performing legal-related actions, significantly improving access to justice.
Contribution
This work introduces a novel multimodal LLM-based web agent framework that automates legal information retrieval and procedural tasks, enhancing access to justice for ordinary citizens.
Findings
Achieved a peak success rate of 86.7% in real-world legal tasks.
Demonstrated high autonomy in complex legal web interactions.
Validated effectiveness through a comprehensive benchmark with 15 tasks.
Abstract
Access to justice remains a global challenge, with many citizens still finding it difficult to seek help from the justice system when facing legal issues. Although the internet provides abundant legal information and services, navigating complex websites, understanding legal terminology, and filling out procedural forms continue to pose barriers to accessing justice. This paper introduces the LegalWebAgent framework that employs a web agent powered by multimodal large language models to bridge the gap in access to justice for ordinary citizens. The framework combines the natural language understanding capabilities of large language models with multimodal perception, enabling a complete process from user query to concrete action. It operates in three stages: the Ask Module understands user needs through natural language processing; the Browse Module autonomously navigates webpages,…
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Taxonomy
TopicsArtificial Intelligence in Law · Multi-Agent Systems and Negotiation · Artificial Intelligence Applications
