Year: 2024
Type: dissertation
Abstract: Traffic insurance law can be a complex domain for ordinary individuals to grasp, thus affecting its accessibility. Although the traffic insurance law itself and precedents are publicly available, it can be challenging to navigate through this information in order to understand one’s rights for compensation in the event of a traffic accident. Recent advancements in Large Language Models (LLM) have provided an accessible way for people to ask curated questions, that the LLM then answers. Thus, the thesis aims to utilize LLM and surrounding technologies to improve the accessibility of traffic insurance law for everyday users trying to navigate this complex legal domain. The research was conducted as a project for an early-stage startup. The thesis developed an agent that employs Retrieval-Augmented Generation (RAG) techniques with LLM. RAG retrieves relevant information from customselected legal documents and uses the information to answer users’ questions. The agent is encapsulated within a web application which provides an accessible way for the user to communicate with the agent. The results were evaluated by a legal expert in a human-feedback manner. The expert indicated that the system could generate satisfactory responses to experts’ questions. By employing RAG techniques the system mitigated hallucination which is commonly related to LLMs, therefore improving the quality of the answer. However, the research also found that the document retrieval was suboptimal, thereby needing more work to improve the retrieval process. The study highlights the potential of using LLMs and RAG techniques to make traffic insurance law more accessible to common people. Furthermore, the research suggests that improving the retrieval mechanism and possibly employing a multi-agent system could further improve the contextual understanding of the system and thus aid individuals in navigating legal processes more effectively. Hence, future research will focus on optimizing retrieval processes and exploring the use of multiple agents.
Source: Aaltodoc (Aalto University)
Author: Ingher, Amir
Language: English
Subfield: Law
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7038731428