arXiv:2605.03052v2 Announce Type: replace Abstract: 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 consider two hypotheses: models could use attention h
The proliferation of advanced Language Models necessitates a deeper understanding of their internal mechanisms, especially regarding nuanced linguistic phenomena like negation, to improve their reliability and safety.
Understanding how LLMs process negation at a mechanistic level is crucial for building more robust, accurate, and trustworthy AI systems, moving beyond superficial performance metrics to address core AI limitations.
This research provides specific insights into LLM internal workings, revealing that current inaccuracies are often due to 'shortcut' behaviors rather than fundamental representational flaws, opening new avenues for model improvement through targeted interventions.
- · AI researchers
- · LLM developers
- · Companies relying on AI accuracy
- · AI safety groups
- · LLMs without mechanistic interpretability
- · Black box AI approaches
Improved accuracy and reliability of AI models in tasks requiring nuanced language understanding.
Accelerated development of explainable AI (XAI) tools and techniques, leading to more transparent and controllable AI.
Enhanced trust in AI systems could broaden their application into high-stakes domains, requiring higher levels of verifiability and safety.
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