PILA：用于大语言模型原生广告的即插即用插入法
PILA: Plug-and-Play Insertion for LLM-native Advertising
AI总结：
研究如何通过自然整合赞助内容实现大语言模型盈利的问题，提出PILA方法，将广告插入重构成条件响应重写问题并解耦为轻量级边车模块，实验证明该方法能在保持回复质量的同时提高广告效果。
AI中文摘要：
如何通过将赞助内容自然地整合到大语言模型（LLM）的回复中来实现盈利，即LLM原生广告，已成为一个关键问题。现有解决方案将广告与内容生成纠缠在单个模型中，与现代仅API或基于工作流程的LLM应用不兼容且会降低原始回复质量。为此提出PILA，将广告插入重新表述为条件响应重写问题并作为轻量级边车模块与上游服务解耦。PILA与模型无关，可无缝集成现有LLM服务。实验表明PILA能提高广告效果并保持回复质量。
英文摘要：
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.
