Factual consistency evaluation is often conducted using Natural Language Inference (NLI)  models, yet these models exhibit limited success in evaluating summaries. Previous work improved such models with synthetic training data. However, the data is typically based on perturbedhuman-writtensummaries, which often differ in their characteristics from realmodel-generatedsummaries and have limited coverage of possible factual errors. Alternatively, large language models (LLMs)  have recently shown promising results in directly evaluating generative tasks, but are too computationally expensive for practical use. Motivated by these limitations, we introduce TrueTeacher, a method for generating synthetic data by annotating diversemodel-generatedsummaries using a LLM. Unlike prior work, TrueTeacher does not rely on human-written summaries, and is multilingual by nature. Experiments on the TRUE benchmark show that a student model trained using our data, substantially outperforms both the state-of-the-art model with similar capacity, and the LLM teacher. In a systematic study, we compare TrueTeacher to existing synthetic data generation methods and demonstrate its superiority and robustness to domain-shift. We also show that our method generalizes to multilingual scenarios. Lastly, we release our large-scale synthetic dataset (1.4M examples) , generated using TrueTeacher, and a checkpoint trained on this data.111Our dataset and model are available at:https://github.com/google-research/google-research/tree/master/true_teacher