  Natural Language Inference (NLI) remains an important benchmark task for
LLMs. NLI datasets are a springboard for transfer learning to other semantic
tasks, and NLI models are standard tools for identifying the faithfulness of
model-generated text. There are several large scale NLI datasets today, and
models have improved greatly by hill-climbing on these collections. Yet their
realistic performance on out-of-distribution/domain data is less
well-understood. We explore the opportunity for synthetic high-quality datasets
to adapt NLI models for zero-shot use in downstream applications across new and
unseen text domains. We demonstrate a new approach for generating NLI data in
diverse domains and lengths, so far not covered by existing training sets. The
resulting examples have meaningful premises, the hypotheses are formed in
creative ways rather than simple edits to a few premise tokens, and the labels
have high accuracy. We show that models trained on this data ($685$K synthetic
examples) have the best generalization to completely new downstream test
settings. On the TRUE benchmark, a T5-small model trained with our data
improves around $7\%$ on average compared to training on the best alternative
dataset. The improvements are more pronounced for smaller models, while still
meaningful on a T5 XXL model. We also demonstrate gains on test sets when
in-domain training data is augmented with our domain-general synthetic data.
