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- W4385573401 abstract "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." @default.
- W4385573401 created "2023-08-05" @default.
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- W4385573401 date "2022-01-01" @default.
- W4385573401 modified "2023-09-24" @default.
- W4385573401 title "Language Models Are Poor Learners of Directional Inference" @default.
- W4385573401 doi "https://doi.org/10.18653/v1/2022.findings-emnlp.64" @default.
- W4385573401 hasPublicationYear "2022" @default.
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