%0 Conference Paper %1 10.1145/3627508.3638316 %A Zelch, Ines %A Hagen, Matthias %A Potthast, Martin %B Proceedings of the 2024 Conference on Human Information Interaction and Retrieval %C New York, NY, USA %D 2024 %I Association for Computing Machinery %K Advertising Generative LLMs, Search information retrieval, %P 142–152 %R 10.1145/3627508.3638316 %T A User Study on the Acceptance of Native Advertising in Generative IR %U https://doi.org/10.1145/3627508.3638316 %X Commercial conversational search engines need a business model. Since advertising is the main source of revenue for “traditional” ten-blue-links web search, ads are not an unlikely option for conversational search either. In traditional web search, ads are usually placed above organic search results. However, large language models (LLMs) may be dynamically prompted to blend product placements with “organic” conversational responses, similar to native advertising in journalism. This type of advertising can be very difficult to recognize, depending on how subtly it is integrated and disclosed. To raise awareness of this potential development, we analyze the capabilities of current LLMs to blend ads with generative search results. In a user study, we ask people about the perceived quality of (emulated) search results in different advertising scenarios. In a substantial number of cases, our survey participants do not notice brand or product placements when they do not expect them. Thus, our results show the potential of LLMs to subtly mix advertising with generated search results. This warrants further investigation, for example, to develop appropriate advertising disclosure rules, and to detect advertising in generated results. Our research also raises broader concerns about whether commercial or open-source generative models can be trusted not to be fine-tuned to generate ads rather than “genuine” responses. %@ 9798400704345

@inproceedings{10.1145/3627508.3638316, abstract = {Commercial conversational search engines need a business model. Since advertising is the main source of revenue for “traditional” ten-blue-links web search, ads are not an unlikely option for conversational search either. In traditional web search, ads are usually placed above organic search results. However, large language models (LLMs) may be dynamically prompted to blend product placements with “organic” conversational responses, similar to native advertising in journalism. This type of advertising can be very difficult to recognize, depending on how subtly it is integrated and disclosed. To raise awareness of this potential development, we analyze the capabilities of current LLMs to blend ads with generative search results. In a user study, we ask people about the perceived quality of (emulated) search results in different advertising scenarios. In a substantial number of cases, our survey participants do not notice brand or product placements when they do not expect them. Thus, our results show the potential of LLMs to subtly mix advertising with generated search results. This warrants further investigation, for example, to develop appropriate advertising disclosure rules, and to detect advertising in generated results. Our research also raises broader concerns about whether commercial or open-source generative models can be trusted not to be fine-tuned to generate ads rather than “genuine” responses.}, added-at = {2024-09-10T10:41:24.000+0200}, address = {New York, NY, USA}, author = {Zelch, Ines and Hagen, Matthias and Potthast, Martin}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/28f65b325dbe228a5c4f262948a82c40f/scadsfct}, booktitle = {Proceedings of the 2024 Conference on Human Information Interaction and Retrieval}, doi = {10.1145/3627508.3638316}, interhash = {b6df907bd4334fb9fd6b302c4f018acf}, intrahash = {8f65b325dbe228a5c4f262948a82c40f}, isbn = {9798400704345}, keywords = {Advertising Generative LLMs, Search information retrieval,}, location = {Sheffield, United Kingdom}, numpages = {11}, pages = {142–152}, publisher = {Association for Computing Machinery}, series = {CHIIR '24}, timestamp = {2024-09-10T10:45:25.000+0200}, title = {A User Study on the Acceptance of Native Advertising in Generative IR}, url = {https://doi.org/10.1145/3627508.3638316}, year = 2024 }