Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning Through Trap Problems

This research paper investigates the compositional reasoning limitations of large language models (LLMs) in mathematical problem-solving by introducing 'trap problems' that expose systematic failures. The study provides empirical evidence of deficiencies in multi-step logical composition and offers insights into improving LLM robustness.