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Article overview
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A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models | | Tiwalayo Eisape
; MH Tessler
; Ishita Dasgupta
; Fei Sha
; Sjoerd van Steenkiste
; Tal Linzen
; | | Date: |
1 Nov 2023 | | Abstract: | A central component of rational behavior is logical inference: the process of
determining which conclusions follow from a set of premises. Psychologists have
documented several ways in which humans’ inferences deviate from the rules of
logic. Do language models, which are trained on text generated by humans,
replicate these biases, or are they able to overcome them? Focusing on the case
of syllogisms -- inferences from two simple premises, which have been studied
extensively in psychology -- we show that larger models are more logical than
smaller ones, and also more logical than humans. At the same time, even the
largest models make systematic errors, some of which mirror human reasoning
biases such as ordering effects and logical fallacies. Overall, we find that
language models mimic the human biases included in their training data, but are
able to overcome them in some cases. | | Source: | arXiv, 2311.00445 | | Services: | Forum | Review | PDF | Favorites | |
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