  The reasoning abilities of Large Language Models (LLMs) are becoming a
central focus of study in NLP. In this paper, we consider the case of
syllogistic reasoning, an area of deductive reasoning studied extensively in
logic and cognitive psychology. Previous research has shown that pre-trained
LLMs exhibit reasoning biases, such as $\textit{content effects}$, avoid
answering that $\textit{no conclusion follows}$, display human-like
difficulties, and struggle with multi-step reasoning. We contribute to this
research line by systematically investigating the effects of chain-of-thought
reasoning, in-context learning (ICL), and supervised fine-tuning (SFT) on
syllogistic reasoning, considering syllogisms with conclusions that support or
violate world knowledge, as well as ones with multiple premises. Crucially, we
go beyond the standard focus on accuracy, with an in-depth analysis of the
conclusions generated by the models. Our results suggest that the behavior of
pre-trained LLMs can be explained by heuristics studied in cognitive science
and that both ICL and SFT improve model performance on valid inferences,
although only the latter mitigates most reasoning biases without harming model
consistency.
