Your AI knows the correct answer to a negative question, but it purposefully ignores the truth to take a lazy shortcut.
Language models have internal components that process negation perfectly, yet they still provide wrong answers in the final output. This failure happens because late-layer attention modules override the model's correct internal logic in favor of simple pattern matching. The AI effectively decides that following a common pattern is easier than sticking to the truth it has already found. This reveals a lazy behavior in neural networks where the final output head is the weakest link in the reasoning chain. Improving AI performance might require forcing the model to trust its internal logic over its surface-level habits.
How Language Models Process Negation
arXiv · 2605.03052
We study how Large Language Models (LLMs) process negation mechanistically. First, we establish that even though open-weight models often provide wrong answers to questions involving negation, they do possess internal components that process negation correctly. Their poor accuracy is due to late-layer attention behavior that promotes simple shortcuts; ablating those attention modules greatly improves accuracy on negation-related questions. Second, we uncover how models process negation. We consi