Can Transformers Reason in Fragments of Natural Language?

• URL: http://arxiv.org/abs/2211.05417v1
• Date: Thu, 10 Nov 2022 08:46:53 GMT
• Title: Can Transformers Reason in Fragments of Natural Language?
• Authors: Viktor Schlegel, Kamen V. Pavlov, Ian Pratt-Hartmann
• Abstract summary: State-of-the-art deep-learning-based approaches to Natural Language Processing (NLP) are credited with various capabilities that involve reasoning with natural language texts. We study the detection of formally valid inferences in controlled fragments of natural language for which the satisfiability problem becomes increasingly complex.
• Score: 2.1485350418225244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State-of-the-art deep-learning-based approaches to Natural Language Processing (NLP) are credited with various capabilities that involve reasoning with natural language texts. In this paper we carry out a large-scale empirical study investigating the detection of formally valid inferences in controlled fragments of natural language for which the satisfiability problem becomes increasingly complex. We find that, while transformer-based language models perform surprisingly well in these scenarios, a deeper analysis re-veals that they appear to overfit to superficial patterns in the data rather than acquiring the logical principles governing the reasoning in these fragments.

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