检测对话式搜索中生成的原生广告
Detecting Generated Native Ads in Conversational Search
AI总结：
本文构建Webis Generated Native Ads 2024数据集，研究用LLMs或微调句子变换器检测对话式搜索响应中自动插入的原生广告，发现句子变换器精确率和召回率超0.9，而LLMs表现不佳。
摘要：
诸如YouChat和Microsoft Copilot等对话式搜索引擎使用大语言模型（LLMs）来生成对查询的响应。只需再迈出一小步，就可以让同一技术在生成的响应中插入广告——而不是将广告单独放置在响应旁边。插入的广告会让人联想到原生广告和产品植入，这两者都是非常有效的微妙且具有操纵性的广告形式。考虑到LLMs相关的高计算成本，提供商需要为此开发可持续的商业模式，对话式搜索引擎的用户很可能在不久的将来就会面对生成的原生广告。因此，在本文中，我们迈出第一步，研究LLMs是否也可以用作一种对策，即用于屏蔽生成的本地广告。我们编制了Webis Generated Native Ads 2024数据集，其中包含查询和自动插入广告的生成响应，并评估LLMs或微调后的句子变换器（sentence transformers）能否检测广告。在我们的实验中，所研究的LLMs难以完成该任务，但句子变换器取得了超过0.9的精确率和召回率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
