
Dataset Summary This dataset was created to train ad blocking systems on the task of identifying advertisements in the responses of large language models (LLMs) and search engines that use retrieval-augmented generation (RAG). It is the successor of the Webis Generated Native Ads 2024 dataset. Citation @misc{heineking:2025, author = {Sebastian Heineking and Ines Zelch and Wilhelm Pertsch and Christian Deubel and Matthias Hagen and Martin Potthast}, title = {{Webis Generated Native Ads 2025}}, doi = {10.5281/zenodo.16941607}, year = 2025}
LLM, Advertising, Retrieval-Augmented-Generation
LLM, Advertising, Retrieval-Augmented-Generation
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
