# Detecting Generated Native Ads in Conversational Search

arXiv: https://arxiv.org/abs/2402.04889 · HTML: https://mlsanity.com/paper/2402.04889/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 7 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Comparing Traditional and LLM-based Search for Consumer Choice: A Randomized Experiment](https://mlsanity.com/paper/2307.03744/) (2023)
- [Query-Variant Advertisement Text Generation with Association Knowledge](https://mlsanity.com/paper/2004.06438/) (2020)
- [An Empirical Investigation On Search Engine Ad Disclosure](https://mlsanity.com/paper/1710.08389/) (2017)

## Built on by (most cited first, 10 of 10)

- [GEM-Bench: A Benchmark for Ad-Injected Response Generation within Generative Engine Marketing](https://mlsanity.com/paper/2509.14221/) (2025)
- [Machine-Readable Ads: Accessibility and Trust Patterns for AI Web Agents interacting with Online Advertisements](https://mlsanity.com/paper/2507.12844/) (2025)
- [NaiAD: Initiate Data-Driven Research for LLM Advertising](https://mlsanity.com/paper/2605.09918/) (2026)
- [TeamCMU at Touch'e: Adversarial Co-Evolution for Advertisement Integration and Detection in Conversational Search](https://mlsanity.com/paper/2507.00509/) (2025)
- [Generative AI Advertising as a Problem of Trustworthy Commercial Intervention](https://mlsanity.com/paper/2605.18673/) (2026)
- [JU-NLP at Touch'e: Covert Advertisement in Conversational AI-Generation and Detection Strategies](https://mlsanity.com/paper/2509.14256/) (2025)
- [Detecting RAG Advertisements Across Advertising Styles](https://mlsanity.com/paper/2603.04925/) (2026)
- [AdsWorldEngine: A Self-Evolving Conversational Advertising Agent through Orchestrator and Tool Coevolution](https://mlsanity.com/paper/2608.13833/) (2026)
- [Token-Level Advertising](https://mlsanity.com/paper/2608.27382/) (2026)
- [LLMAdBench: A Human Preference Benchmark for Advertising in LLM Responses](https://mlsanity.com/paper/2609.32533/) (2026)

## Abstract (arXiv)

Conversational search engines such as YouChat and Microsoft Copilot use large language models (LLMs) to generate responses to queries. It is only a small step to also let the same technology insert ads within the generated responses - instead of separately placing ads next to a response. Inserted ads would be reminiscent of native advertising and product placement, both of which are very effective forms of subtle and manipulative advertising. Considering the high computational costs associated with LLMs, for which providers need to develop sustainable business models, users of conversational search engines may very well be confronted with generated native ads in the near future. In this paper, we thus take a first step to investigate whether LLMs can also be used as a countermeasure, i.e., to block generated native ads. We compile the Webis Generated Native Ads 2024 dataset of queries and generated responses with automatically inserted ads, and evaluate whether LLMs or fine-tuned sentence transformers can detect the ads. In our experiments, the investigated LLMs struggle with the task but sentence transformers achieve precision and recall values above 0.9.