PILA: Plug-and-Play Insertion for LLM-native Advertising

Researchers propose PILA, a plug-and-play insertion module for LLM-native advertising that decouples ad insertion from content generation as a lightweight sidecar, enabling model-agnostic integration without compromising response quality. Experiments show PILA improves ad effectiveness while preserving naturalness across diverse upstream models.

arXiv:2607.25590v1 Announce Type: new Abstract: How to monetize large language models LLMs by naturally integrating sponsored content into their responses, known as LLM-native advertising, has recently emerged as a critical problem. However, existing solutions entangle advertising with content generation inside a single model, which is incompatible with modern API-only or workflow-based LLM applications and inevitably compromises the original response quality. To address this, we propose PILA, which reformulates ad insertion as a conditional response rewriting problem and decouples it from the upstream service as a lightweight sidecar module. PILA is model-agnostic and can be seamlessly integrated with existing LLM services without modifying the base model or its workflow. It also exposes a controllable trade-off between user-side naturalness and ad-side exposure, offering a practical interface for downstream pricing and deployment. Experiments across diverse upstream models show that \pila consistently improves ad effectiveness while preserving response quality, highlighting its promise as a practical solution for LLM-native advertising.