Report
Counterfactual reasoning: Testing language models' understanding of hypothetical scenarios
| العنوان: | Counterfactual reasoning: Testing language models' understanding of hypothetical scenarios |
|---|---|
| المؤلفون: | Li, Jiaxuan, Yu, Lang, Ettinger, Allyson |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language |
| الوصف: | Current pre-trained language models have enabled remarkable improvements in downstream tasks, but it remains difficult to distinguish effects of statistical correlation from more systematic logical reasoning grounded on the understanding of real world. We tease these factors apart by leveraging counterfactual conditionals, which force language models to predict unusual consequences based on hypothetical propositions. We introduce a set of tests from psycholinguistic experiments, as well as larger-scale controlled datasets, to probe counterfactual predictions from five pre-trained language models. We find that models are consistently able to override real-world knowledge in counterfactual scenarios, and that this effect is more robust in case of stronger baseline world knowledge -- however, we also find that for most models this effect appears largely to be driven by simple lexical cues. When we mitigate effects of both world knowledge and lexical cues to test knowledge of linguistic nuances of counterfactuals, we find that only GPT-3 shows sensitivity to these nuances, though this sensitivity is also non-trivially impacted by lexical associative factors. Comment: Accepted to ACL 2023. arXiv admin note: substantial text overlap with arXiv:2212.03278 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2305.16572 |
| رقم الانضمام: | edsarx.2305.16572 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |