DRAssist: Dispute Resolution Assistance using Large Language Models

• URL: http://arxiv.org/abs/2509.01962v1
• Date: Tue, 02 Sep 2025 05:09:34 GMT
• Title: DRAssist: Dispute Resolution Assistance using Large Language Models
• Authors: Sachin Pawar, Manoj Apte, Girish K. Palshikar, Basit Ali, Nitin Ramrakhiyani,
• Abstract summary: We explore the use of large language models (LLMs) as assistants for the human judge to resolve such disputes.<n>We focus on disputes from two specific domains -- automobile insurance and domain name disputes.
• Score: 1.9708256160559825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Disputes between two parties occur in almost all domains such as taxation, insurance, banking, healthcare, etc. The disputes are generally resolved in a specific forum (e.g., consumer court) where facts are presented, points of disagreement are discussed, arguments as well as specific demands of the parties are heard, and finally a human judge resolves the dispute by often favouring one of the two parties. In this paper, we explore the use of large language models (LLMs) as assistants for the human judge to resolve such disputes, as part of our DRAssist system. We focus on disputes from two specific domains -- automobile insurance and domain name disputes. DRAssist identifies certain key structural elements (e.g., facts, aspects or disagreement, arguments) of the disputes and summarizes the unstructured dispute descriptions to produce a structured summary for each dispute. We then explore multiple prompting strategies with multiple LLMs for their ability to assist in resolving the disputes in these domains. In DRAssist, these LLMs are prompted to produce the resolution output at three different levels -- (i) identifying an overall stronger party in a dispute, (ii) decide whether each specific demand of each contesting party can be accepted or not, (iii) evaluate whether each argument by each contesting party is strong or weak. We evaluate the performance of LLMs on all these tasks by comparing them with relevant baselines using suitable evaluation metrics.

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