Rewrite-to-Rank: Optimizing Ad Visibility via Retrieval-Aware Text Rewriting

• URL: http://arxiv.org/abs/2507.21099v1
• Date: Thu, 03 Jul 2025 05:36:08 GMT
• Title: Rewrite-to-Rank: Optimizing Ad Visibility via Retrieval-Aware Text Rewriting
• Authors: Chloe Ho, Ishneet Sukhvinder Singh, Diya Sharma, Tanvi Reddy Anumandla, Michael Lu, Vasu Sharma, Kevin Zhu,
• Abstract summary: We investigate how LLM-based rewriting of advertisements can improve their ranking in retrieval systems.<n>We introduce a supervised fine-tuning framework with a custom loss balancing semantic relevance and content fidelity.
• Score: 2.743338598862049
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Search algorithms and user query relevance have given LLMs the ability to return relevant information, but the effect of content phrasing on ad visibility remains underexplored. We investigate how LLM-based rewriting of advertisements can improve their ranking in retrieval systems and inclusion in generated LLM responses, without modifying the retrieval model itself. We introduce a supervised fine-tuning framework with a custom loss balancing semantic relevance and content fidelity. To evaluate effectiveness, we propose two metrics: DeltaMRR@K (ranking improvement) and DeltaDIR@K (inclusion frequency improvement). Our approach presents a scalable method to optimize ad phrasing, enhancing visibility in retrieval-based LLM workflows. Experiments across both instruction-based and few-shot prompting demonstrate that PPO trained models outperform both prompt engineering and supervised fine-tuning in most cases, achieving up to a 2.79 DeltaDIR@5 and 0.0073 DeltaMRR@5 in instruction-based prompting. These results highlight the importance of how the ad is written before retrieval and prompt format and reinforcement learning in effective ad rewriting for LLM integrated retrieval systems.

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