Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study
  on Syllogism
Mengyu Ye Tatsuki Kuribayashi Jun Suzuki Goro Kobayashi Hiroaki Funayama
Large language models (LLMs) take advantage of step-by-step reasoning
instructions, e.g., chain-of-thought (CoT) prompting. Building on this, their
ability to perform CoT-style reasoning robustly is of interest from a probing
perspective. In this study, we inspect the step-by-step reasoning ability of
LLMs with a focus on negation, which is a core linguistic phenomenon that is
difficult to process. In particular, we introduce several controlled settings
(e.g., reasoning in case of fictional entities) to evaluate the logical
reasoning abilities of the models. We observed that dozens of modern LLMs were
not robust against lexical negation (e.g., plausible ->implausible) when
performing CoT-style reasoning, and the results highlight unique limitations in
each LLM family.