AdSteer: Native Advertising at Decoding Time for Large Language Models

Monetizing large language model (LLM) services through native advertising is essential for sustaining free or affordable access. Existing approaches mainly rely on prompt-level injection either multi-pass rewriting or finetuning, which compromise response quality or increase inference overhead. We propose **AdSteer**, a decoding-time advertising framework that steers language model's token generation, turning ads content into a natural part of the response. As a lightweight plug-in, AdSteer integrates ads more smoothly into the generation process, introducing appropriate sponsored mentions based on the context. Experiments on GEM-Bench across three application scenarios show that the best-performing AdSteer configuration in each scenario achieves an average improvement of 14.1% in overall score over the strongest baselines and an average of 25% fewer tokens than rewrite-based methods, demonstrating that AdSteer provides an effective and practical solution for native advertising in LLMs.