Source2Synth: Synthetic Data Generation and Curation Grounded in Real
  Data Sources
Authors: name: Alisia Lupidi, name: Carlos Gemmell, name: Nicola Cancedda, name: Jane Dwivedi-Yu, name: Jason Weston, name: Jakob Foerster, name: Roberta Raileanu, name: Maria Lomeli

Large Language Models still struggle in challenging scenarios that leverage
structured data, complex reasoning, or tool usage. In this paper, we propose
Source2Synth: a new method that can be used for teaching LLMs new skills
without relying on costly human annotations. Source2Synth takes as input a
custom data source and produces synthetic data points with intermediate
reasoning steps grounded in real-world sources. Source2Synth improves the
dataset quality by discarding low-quality generations based on their
answerability. We demonstrate the generality of this approach by applying it to
two challenging domains: we test reasoning abilities in multi-hop question
answering (MHQA), and tool usage in tabular question answering (TQA). Our
method improves performance by 25.51% for TQA on WikiSQL and 22.57% for MHQA on
HotPotQA compared to the fine-tuned baselines.