This research reveals how LLMs may adapt to benchmark-specific patterns rather than developing genuine language understanding capabilities.

Key Findings:

• LLMs can appear to excel on benchmarks while relying on surface-level cues rather than deeper comprehension
• The new C-BOD framework detects benchmark overfitting by systematically transforming prompts
• Performance drops on semantically equivalent but rephrased inputs expose limitations in true language understanding

For educators, this research highlights the importance of looking beyond benchmark scores when evaluating LLMs for educational applications. It suggests that current evaluation methods may overestimate LLM capabilities, potentially leading to misaligned expectations when deploying these models in real educational settings.

Forget What You Know about LLMs Evaluations -- LLMs are Like a Chameleon