Speaker:Gazi Nazia Nur

CoralAI: A RAG model to answer Coral-related queries

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, contextually accurate answers to user queries, 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 Similarity, and Answer Correctness to demonstrate its performance. 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.

Committee: Simanta Mitra (major professor) and Gurpur Prabhu