Language Models Are Poor Learners of Directional Inference

Tianyi Li, Mohammad Javad Hosseini, Sabine Weber, Mark Steedman


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.
Anthology ID:
2022.findings-emnlp.64
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
903–921
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.64
DOI:
10.18653/v1/2022.findings-emnlp.64
Bibkey:
Cite (ACL):
Tianyi Li, Mohammad Javad Hosseini, Sabine Weber, and Mark Steedman. 2022. Language Models Are Poor Learners of Directional Inference. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 903–921, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Language Models Are Poor Learners of Directional Inference (Li et al., Findings 2022)
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