Language Models Are Poor Learners of Directional Inference

• URL: http://arxiv.org/abs/2210.04695v1
• Date: Mon, 10 Oct 2022 13:43:16 GMT
• Title: Language Models Are Poor Learners of Directional Inference
• Abstract summary: LMs show limited ability to learn such directional inference. Existing datasets fail to test directionality. Existing LM-prompting models are incompetent directional entailment learners.
• Score: 17.807086499130488
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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