A synthetic data approach for domain generalization of NLI models
Mohammad Javad Hosseini Andrey Petrov Alex Fabrikant Annie Louis
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 present an in-depth exploration of the problem of domain
generalization of NLI models. We demonstrate a new approach for generating
synthetic 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.