A recent study reveals that large language models like GPT-3 struggle with complex reasoning tasks, despite near-perfect accuracy on simpler, in-domain examples. The research shows that Transformers reduce multi-step reasoning to pattern matching, leading to errors in chaining multiple steps. The findings highlight the need for models that can systematically extrapolate beyond their training data for complex tasks.
Read more…