A Mechanistic Interpretation of Syllogistic Reasoning in Auto-Regressive Language Models

Recent studies on logical reasoning in Language Models (LMs) have sparked a debate on whether they can learn systematic reasoning principles during pre-training or merely exploit superficial patterns in the training data. This paper presents a mechanistic interpretation of syllogistic reasoning in LMs to advance the understanding of internal dynamics. Specifically, we present a methodology for circuit discovery aimed at interpreting content-independent reasoning mechanisms. Through two distinct intervention methods, we uncover a sufficient and necessary circuit involving middle-term suppression that elucidates how LMs transfer information to derive valid conclusions from premises. Furthermore, we investigate how belief biases manifest in syllogistic reasoning, finding evidence of partial contamination from additional attention heads responsible for encoding commonsense and contextualized knowledge. Finally, we explore the generalization of the discovered mechanisms across various syllogistic schemes, model sizes and architectures, finding that the identified circuit is sufficient and necessary for the schemes on which the models achieve high downstream accuracy (> 60%), and that the activation patterns apply to models of different families. Overall, our findings suggest that LMs indeed learn transferable content-independent reasoning mechanisms, but that, at the same time, such mechanisms do not involve generalizable and abstract logical primitives, being susceptible to contamination by the same world knowledge acquired during pre-training.

@article{kim2025_2408.08590,
  title={ A Mechanistic Interpretation of Syllogistic Reasoning in Auto-Regressive Language Models },
  author={ Geonhee Kim and Marco Valentino and André Freitas },
  journal={arXiv preprint arXiv:2408.08590},
  year={ 2025 }
}