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
- 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)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2022.findings-emnlp.64.pdf