  We introduce a novel data generation method for contradiction detection,
which leverages the generative power of large language models as well as
linguistic rules. Our vision is to provide a condensed corpus of prototypical
contradictions, allowing for in-depth linguistic analysis as well as efficient
language model fine-tuning. To this end, we instruct the generative models to
create contradicting statements with respect to descriptions of specific
contradiction types. In addition, the model is also instructed to come up with
completely new contradiction typologies. As an auxiliary approach, we use
linguistic rules to construct simple contradictions such as those arising from
negation, antonymy and numeric mismatch. We find that our methods yield
promising results in terms of coherence and variety of the data. Further
studies, as well as manual refinement are necessary to make use of this data in
a machine learning setup.
