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Adapting Large Language Models for Low-Resource Regulated Domains: A Fine-Tuning and Retrieval-Augmented Generation Approach to Insurance Information Delivery in Kenya

2026-05-22

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<title>Abstract</title> <p>Background. Insurance penetration in Kenya stands at roughly 2.3% of GDP, well below the global average of 7.4%. Documented barriers include low financial literacy, affordability, cultural resistance and limited access to trusted, locally relevant information. Large Language Models (LLMs) could in principle deliver scalable, personalised information, but off-the-shelf models perform poo...

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Literature Corpus work: 4c2cabfd-9471-5335-87fd-862c384bc36f
DOI: 10.21203/rs.3.rs-9770645/v1

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