Syllogistic Reasoning

Syllogistic reasoning, a form of deductive logic involving inferences from two premises, is a key area of investigation in understanding both human cognition and the capabilities of large language models (LLMs). Current research focuses on identifying and mitigating biases in LLMs' syllogistic reasoning, comparing their performance to human reasoning, and exploring the underlying mechanisms through techniques like circuit discovery and chain-of-thought prompting. These studies reveal that while LLMs can achieve high accuracy on some syllogisms, they often exhibit human-like biases and struggle with complex or negated statements, highlighting limitations in their ability to perform truly abstract logical reasoning and underscoring the need for improved model architectures and training methods.