Title: AI-driven multi-modal legal dispute question and answer system: legal reasoning and evaluation

Authors: Lin Lin

Addresses: Harbin Finance University, Harbin, China

Abstract: With the growing integration of artificial intelligence (AI) into legal services, AI-driven legal question answering (QA) systems have garnered significant attention. However, most existing systems focus on legal knowledge retrieval and text generation, often failing to effectively address complex legal disputes that require nuanced reasoning based on core legal terms. To address this gap, we propose a novel AI-driven core keyword legal dispute question answering system (CKLQ), which combines core keyword extraction, legal dispute categorisation, and legal knowledge retrieval for handling a variety of legal disputes. It uses a multi-modal reasoning framework to enhance performance in categorising legal disputes and accurately extracting core keywords from complex legal texts. We evaluate CKLQ using accuracy, F1-score, answer accuracy, and explanation accuracy across three comprehensive legal datasets: LawBench, Legal Case Corpus, and Contract Disputes. Performance is validated against five state-of-the-art baseline models including LegalBERT, LexGLUE, LawGPT, LawBench Evaluation (original), and LegalBERT+RAG.

Keywords: core keyword extractionl; legal dispute classification; legal reasoning; multi-modal reasoning; legal question answering system.

DOI: 10.1504/IJBIDM.2026.155249

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.4/5/6, pp.381 - 399

Received: 18 Aug 2025
Accepted: 06 Jan 2026
Published online: 29 Jul 2026 *

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