  Recent efforts within the AI community have yielded impressive results
towards "soft theorem proving" over natural language sentences using language
models. We propose a novel, generative adversarial framework for probing and
improving these models' reasoning capabilities. Adversarial attacks in this
domain suffer from the logical inconsistency problem, whereby perturbations to
the input may alter the label. Our Logically consistent AdVersarial Attacker,
LAVA, addresses this by combining a structured generative process with a
symbolic solver, guaranteeing logical consistency. Our framework successfully
generates adversarial attacks and identifies global weaknesses common across
multiple target models. Our analyses reveal naive heuristics and
vulnerabilities in these models' reasoning capabilities, exposing an incomplete
grasp of logical deduction under logic programs. Finally, in addition to
effective probing of these models, we show that training on the generated
samples improves the target model's performance.
