Ever get that nagging feeling you’ve read something before? That's a common problem with massive datasets, especially in research. Imagine an economist trying to build a new model for predicting market trends. They gather millions of research papers, but hidden within are duplicates and near-duplicates, subtly different versions of the same study. This "data duplication" can skew the results, leading to inaccurate models. A new study from the World Bank tackles this challenge head-on, exploring how to efficiently remove these duplicates from a massive dataset of economic research paper titles. Researchers tested various methods, from simple string comparisons (like checking for typos) to advanced semantic analysis using AI. They used a powerful AI model called SBERT, which understands the *meaning* of text, not just the words themselves, to identify papers with similar underlying concepts. The findings suggest that true duplicates aren’t as common as you might think, but that identifying *near* duplicates is where things get tricky. Even papers with similar titles can have distinct semantic differences that are hard to spot with traditional methods. The research is still in its early stages, but it highlights the potential of using AI not just for generating research, but also for organizing and refining it. This is a vital step towards ensuring the data used in economic modeling is as accurate and reliable as possible, paving the way for better predictions and more informed policy decisions. Future improvements could include tailoring AI models to the unique language of economics and developing methods to remove duplicates while keeping the important diversity of research intact.