Competitive Analysis of Embedding Models in Retrieval-Augmented Generation for Indian Motor Vehicle Law Chat Bots

dc.contributor.advisorHoare, Terri
dc.contributor.authorMohanan, Monisha
dc.date.accessioned2024-04-04T14:08:18Z
dc.date.available2024-04-04T14:08:18Z
dc.date.issued2024
dc.description.abstractThis study evaluates eight embedding models in Retrieval-Augmented Generation (RAG) systems for a chatbot tailored to Indian Motor Vehicle Law. The models examined are OpenAIEmbeddings, UAE-Large-V1, all-MiniLM-L6-v2, all-distilroberta-v1, all-mpnet-base-v2, bge-large-en-v1.5, ember-v1, and gte-large. Through Cosine Similarity and ROUGE metrics, the analysis distinguishes OpenAIEmbeddings and gte-large for their superior semantic understanding. These models showed remarkable alignment with expert-generated answers, indicating their efficacy in AI-driven legal assistance. The study's outcomes underscore the importance of embedding model selection in legal chatbot development, focusing on semantic comprehension capabilities. This research is pivotal for enhancing AI legal assistance, offering insights into the effective integration of embedding models in legal technology applications.
dc.identifier.citationMohanan, M. (2024) Competitive Analysis of Embedding Models in Retrieval-Augmented Generation for Indian Motor Vehicle Law Chat Bots. Master's Thesis, Dublin Business School.
dc.identifier.urihttps://hdl.handle.net/10788/4531
dc.language.isoen
dc.publisherDublin Business School
dc.rights.holderCopyright, the Author
dc.rights.urihttp://www.esource.dbs.ie/copyright
dc.subjectMotor vehicles--Standards--Law and legislation
dc.subjectIndian business enterprises--Law and legislation
dc.subjectChatbots
dc.titleCompetitive Analysis of Embedding Models in Retrieval-Augmented Generation for Indian Motor Vehicle Law Chat Bots
dc.typeThesis
dc.type.degreelevelMSc
dc.type.degreenameMSc in Artificial Intelligence
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