  We examine LMs' competence of directional predicate entailments by supervised
fine-tuning with prompts. Our analysis shows that contrary to their apparent
success on standard NLI, LMs show limited ability to learn such directional
inference; moreover, existing datasets fail to test directionality, and/or are
infested by artefacts that can be learnt as proxy for entailments, yielding
over-optimistic results. In response, we present BoOQA (Boolean Open QA), a
robust multi-lingual evaluation benchmark for directional predicate
entailments, extrinsic to existing training sets. On BoOQA, we establish
baselines and show evidence of existing LM-prompting models being incompetent
directional entailment learners, in contrast to entailment graphs, however
limited by sparsity.
