CoralAI: A RAG model to answer Coral-related queries
Date
2024-12
Authors
Nur, Gazi Nazia
Major Professor
Mitra, Simanta
Advisor
Committee Member
Prabhu, Gurpur
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Altmetrics
Abstract
The expansive volume of coral reef literature presents significant challenges for researchers seeking timely and accurate answers to domain-specific questions. Traditional reliance on general-purpose large language models (LLMs) often leads to incomplete or inaccurate responses, which can undermine scientific rigor. In response, we introduce CoralAI, a Retrieval-Augmented Generation (RAG) model designed specifically for coral reef research. CoralAI integrates a carefully curated database of high-quality research papers on coral ecosystems, enabling it to generate precise and contextually accurate answers to user queries that are grounded in verified scientific literature. The model begins by splitting research papers into smaller text chunks. Through a retrieval mechanism, the model then selects and ranks text chunks most relevant to each query, ensuring high degrees of relevance and similarity. These selected chunks are then synthesized into concise summaries of relevant chunks based on the given query, accompanied by citations that allow researchers to trace information back to the original sources. CoralAI is evaluated on metrics such as Context Recall, Context Precision, Faithfulness, Answer Relevance, and Answer Semantic Similarity to demonstrate its performance. We evaluate these metrics using queries generated both by humans and LLMs. Additionally, a user-friendly interface has been developed to provide seamless access to the model, allowing researchers to benefit from its insights without needing to engage with complex backend processes.
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creative component
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Rights Statement
Attribution 3.0 United States
Copyright
2025