SoccerRAG: Multimodal Soccer Information Retrieval via Natural Queries

• URL: http://arxiv.org/abs/2406.01273v2
• Date: Mon, 22 Jul 2024 06:42:44 GMT
• Title: SoccerRAG: Multimodal Soccer Information Retrieval via Natural Queries
• Authors: Aleksander Theo Strand, Sushant Gautam, Cise Midoglu, Pål Halvorsen,
• Abstract summary: SoccerRAG is an innovative framework designed to harness the power of Retrieval Augmented Generation (RAG) and Large Language Models (LLMs) By leveraging a multimodal dataset, SoccerRAG supports dynamic querying and automatic data validation. Our evaluations indicate that SoccerRAG effectively handles complex queries, offering significant improvements over traditional retrieval systems.
• Score: 42.095162323265676
• License:
• Abstract: The rapid evolution of digital sports media necessitates sophisticated information retrieval systems that can efficiently parse extensive multimodal datasets. This paper introduces SoccerRAG, an innovative framework designed to harness the power of Retrieval Augmented Generation (RAG) and Large Language Models (LLMs) to extract soccer-related information through natural language queries. By leveraging a multimodal dataset, SoccerRAG supports dynamic querying and automatic data validation, enhancing user interaction and accessibility to sports archives. Our evaluations indicate that SoccerRAG effectively handles complex queries, offering significant improvements over traditional retrieval systems in terms of accuracy and user engagement. The results underscore the potential of using RAG and LLMs in sports analytics, paving the way for future advancements in the accessibility and real-time processing of sports data.

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