Large Language Models still struggle in challenging scenarios that leverage structured data, complex reasoning, or tool usage. In this paper, we proposeSource2Synth: a new method that can be used for teaching LLMs new skills without
relying on costly human annotations.Source2Synthtakes as input a custom data source and produces synthetic data points with intermediate reasoning steps grounded in real-world sources.Source2Synthimproves 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.