Stop Early, Spend Less: Hidden-State Probes as a Practical Recipe for Streaming Moderation of LLM Outputs

The paper introduces a novel approach for real-time moderation of large language model outputs using lightweight token-level probes that operate on internal hidden states, eliminating the need for a separate moderation model. This method allows for sub-millisecond per-token safety checks during the decoding process, significantly reducing computational overhead compared to traditional post-generation moderation techniques. By leveraging internal activations, the proposed system enables continuous monitoring and intervention, which is crucial for deploying LLMs in user-facing applications while maintaining performance efficiency.