Recitation over Reasoning: How Cutting-Edge Language Models Can Fail on Elementary School-Level Reasoning Problems?
Kai Yan, Yufei Xu, Zhengyin Du, Xuesong Yao, Zheyu Wang, Xiaowen Guo, Jiecao Chen
2025-04-02
Summary
This paper is about how AI models that seem really smart can actually fail at simple reasoning tasks because they're just memorizing answers instead of truly understanding the problem.
What's the problem?
We don't know if AI models are actually intelligent or if they're just reciting solutions they've seen before.
What's the solution?
The researchers created a test to see if AI models could still solve problems when the details were slightly changed. If the model suddenly fails with a minor change, it's likely just reciting.
Why it matters?
This work matters because it suggests we need to be careful about how we evaluate AI and make sure it's actually intelligent, not just good at memorization.
Abstract
The rapid escalation from elementary school-level to frontier problems of the difficulty for LLM benchmarks in recent years have weaved a miracle for researchers that we are only inches away from surpassing human intelligence. However, is the LLMs' remarkable reasoning ability indeed comes from true intelligence by human standards, or are they simply reciting solutions witnessed during training at an Internet level? To study this problem, we propose RoR-Bench, a novel, multi-modal benchmark for detecting LLM's recitation behavior when asked simple reasoning problems but with conditions subtly shifted, and conduct empirical analysis on our benchmark. Surprisingly, we found existing cutting-edge LLMs unanimously exhibits extremely severe recitation behavior; by changing one phrase in the condition, top models such as OpenAI-o1 and DeepSeek-R1 can suffer 60% performance loss on elementary school-level arithmetic and reasoning problems. Such findings are a wake-up call to the LLM community that compels us to re-evaluate the true intelligence level of cutting-edge LLMs.