response-rewriting

This paper evaluates end-to-end trade-offs in moderation for conversational AI, comparing filter placement (input, response, both) and actions (blocking vs rewriting) using customer-outcome metrics like Usefulness and Harmful Exposure instead of component accuracy.

PILA reformulates LLM-native advertising as a conditional response rewriting problem, decoupling ad insertion from upstream generation via a lightweight, model-agnostic sidecar module that preserves response quality while enabling controllable ad exposure.