UC San Diego Undergraduate Library Research Prize

Winner 2024 Data & GIS Category, nominated by Professor Colin Jemmott (Halıcıoğlu Data Science Institute)

Although the amount of resources available on the internet seems to be growing daily, it’s becoming increasingly challenging to navigate the sea of information. While one might expect the influx of articles to make it easier to answer questions, the information accessible is often written well after the historical events have concluded, reflecting modern perspectives and interpretations. When answering historical questions, people often tend to rely on a simple Google search, which leads to websites such as Wikipedia. These sites can be problematic due to the unreliability of sources and the tendency to project 21st-century viewpoints onto historical information. These tools provide users with a surface-level summary that lacks the historical nuance and contextualization that is needed for a thorough understanding of the matter at hand. This project aims to address this gap in relevant and credible information retrieval. This project accomplishes these goals through Retrieval Augmented Generation (RAG), which is a combination of a search algorithm andLarge Language Models (LLMs) to answer user queries. Retrieval Augmented Generation avoids the limitations of traditional search functions, which can struggle with large amounts of data, causing inaccurate or oversimplified results. A user can ask a question about historical events, and our model will search, retrieve, and synthesize data, ultimately responding to the user’s questions using a diverse array of historical news sources.