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        <title>Language Models Are Poor Learners of Directional Inference</title>
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        <namePart type="given">Tianyi</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.64</identifier>
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%0 Conference Proceedings
%T Language Models Are Poor Learners of Directional Inference
%A Li, Tianyi
%A Hosseini, Mohammad Javad
%A Weber, Sabine
%A Steedman, Mark
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F li-etal-2022-language
%X 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.
%R 10.18653/v1/2022.findings-emnlp.64
%U https://aclanthology.org/2022.findings-emnlp.64
%U https://doi.org/10.18653/v1/2022.findings-emnlp.64
%P 903-921

Markdown (Informal)

[Language Models Are Poor Learners of Directional Inference](https://aclanthology.org/2022.findings-emnlp.64) (Li et al., Findings 2022)

• Language Models Are Poor Learners of Directional Inference (Li et al., Findings 2022)

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.