LEMON: Zero-shot misbehavior monitoring for new LLMs without retraining
In brief
- LEMON monitors LLM errors in real-time using their attention maps.
- The model works without retraining for each new LLM.
- Improves detection performance by up to 14 ROC-AUC points across 16 models.
Researchers introduce LEMON, a real-time monitoring system for large language models using their attention maps. The approach detects hallucinations and jailbreaks without retraining for each new model. Tests across 16 LLMs and six datasets show up to 14 ROC-AUC points improvement over existing methods.
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