Stop Early, Spend Less: Hidden-State Probes as a Practical Recipe for Streaming Moderation of LLM Outputs
arXiv:2606.10487v1 Announce Type: new Abstract: Deploying large language models in user-facing systems requires efficient output safety filtering. Existing approaches typically rely on a separate moderation model applied after generation, which doubles inference cost and only detects violations after generation completes. We observe that the signal needed for moderation is already present in the model hidden states. Based on this, we train lightweight token-level probes that operate directly on internal activations, producing per-token safety scores that can be aggregated for both offline eval
The increasing deployment of LLMs in user-facing applications highlights the critical need for efficient and real-time moderation solutions.
This research addresses a key limitation in LLM deployment by offering a method to significantly reduce moderation costs and latency, directly impacting the economic viability and safety of large-scale AI applications.
Moderation shifts from a post-generation, high-cost process to an in-generation, low-cost method leveraging internal model states.
- · LLM developers
- · AI-powered social platforms
- · Companies deploying user-facing AI
- · AI safety researchers
- · Traditional post-generation moderation services
- · Developers solely relying on external moderation APIs
Reduced operational costs for LLM-based services, making them more accessible and scalable.
Improved real-time safety and ethical performance of AI systems, potentially accelerating wider adoption in sensitive domains.
Enhanced ability to fine-tune and control LLM behavior from within, leading to more robust and aligned AI models.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG