Online Safety Monitoring for LLMs
Mona Schirmer, Metod Jazbec, Alexander Timans, Christian Naesseth, Maja Waldron, Eric Nalisnick
Why It Matters
What makes this one worth your time
Ensuring the safety of LLM outputs in real-time is crucial for their deployment in sensitive applications, and this paper offers a potentially effective yet simple solution.
A simple real-time safety monitor for LLMs using thresholded verifier signals.
Summary
The paper proposes a simple real-time monitoring system for large language models (LLMs) that uses an external verifier model to generate a signal, which is then thresholded to raise alarms for unsafe outputs. The threshold is calibrated using risk control, and the system is tested on mathematical reasoning and red teaming datasets, showing competitive performance with more advanced methods.
Key contributions
- Development of a real-time monitoring system for LLMs using verifier signals.
- Calibration of alarm thresholds through risk control.
- Empirical evaluation on mathematical reasoning and red teaming datasets.
Notable insights
- Using a thresholded verifier signal for real-time monitoring is a straightforward yet effective approach.
- Calibration via risk control can enhance the reliability of safety monitoring systems.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2607.02510v1 Announce Type: new Abstract: Despite alignment training, LLMs remain prone to generating unsafe outputs at deployment time. Monitoring outputs online and raising an alarm when safety can no longer be assumed is therefore critical. We study a simple real-time monitor that turns a verifier signal from an external model into an alarm decision by thresholding, with the threshold calibrated via risk control. In experiments on mathematical reasoning and red teaming datasets, we show that this simple design is competitive with more advanced monitors based on sequential hypothesis testing.