arxiv preprint - Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources

In this episode, we discuss Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources by Alisia Lupidi, Carlos Gemmell, Nicola Cancedda, Jane Dwivedi-Yu, Jason Weston, Jakob Foerster, Roberta Raileanu, Maria Lomeli. The paper presents Source2Synth, a method designed to enhance Large Language Models (LLMs) by generating synthetic data with intermediate reasoning steps, grounded in real-world sources, to improve performance without costly human annotations. Source2Synth also filters out low-quality data points to ensure high-quality datasets. The method demonstrates significant improvements in performance for multi-hop question answering and tool usage in tabular question answering, with respective boosts of 22.57% on HotPotQA and 25.51% on WikiSQL.