Detecting Generated Native Ads in Conversational Search

Conversational search engines such as YouChat and Microsoft Copilot use large language models (LLMs) to generate answers to queries. It is only a small step to also use this technology to generate and integrate advertising within these answers - instead of placing ads separately from the organic search results. This type of advertising is reminiscent of native advertising and product placement, both of which are very effective forms of subtle and manipulative advertising. It is likely that information seekers will be confronted with such use of LLM technology in the near future, especially when considering the high computational costs associated with LLMs, for which providers need to develop sustainable business models. This paper investigates whether LLMs can also be used as a countermeasure against generated native ads, i.e., to block them. For this purpose we compile a large dataset of ad-prone queries and of generated answers with automatically integrated ads to experiment with fine-tuned sentence transformers and state-of-the-art LLMs on the task of recognizing the ads. In our experiments sentence transformers achieve detection precision and recall values above 0.9, while the investigated LLMs struggle with the task.

Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss

Active learning (AL) reduces the amount of labeled data needed to train a machine learning model by intelligently choosing which instances to label. Classic pool-based AL requires all data to be present in a datacenter, which can be challenging with the increasing amounts of data needed in deep learning. However, AL on mobile devices and robots, like autonomous cars, can filter the data from perception sensor streams before reaching the datacenter. We exploited the temporal properties for such image streams in our work and proposed the novel temporal predicted loss (TPL) method. To evaluate the stream-based setting properly, we introduced the GTA V streets and the A2D2 streets dataset and made both publicly available. Our experiments showed that our approach significantly improves the diversity of the selection while being an uncertainty-based method. As pool-based approaches are more common in perception applications, we derived a concept for comparing pool-based and stream-based AL, where TPL out-performed state-of-the-art pool- or stream-based approaches for different models. TPL demonstrated a gain of 2.5 precept points (pp) less required data while being significantly faster than pool-based methods.

The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web Archives

The Archive Query Log (AQL) is a previously unused, comprehensive query log collected at the Internet Archive over the last 25 years. Its first version includes 356 million queries, 166 million search result pages, and 1.7 billion search results across 550 search providers. Although many query logs have been studied in the literature, the search providers that own them generally do not publish their logs to protect user privacy and vital business data. Of the few query logs publicly available, none combines size, scope, and diversity. The AQL is the first to do so, enabling research on new retrieval models and (diachronic) search engine analyses. Provided in a privacy-preserving manner, it promotes open research as well as more transparency and accountability in the search industry.

FH-SWF SG at GermEval 2021: Using Transformer-Based Language Models to Identify Toxic, Engaging, & Fact-Claiming Comments

In this paper we describe the methods we used for our submissions to the GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments. For all three subtasks we fine-tuned freely available transformer-based models from the Huggingface model hub. We evaluated the performance of various pre-trained models after fine-tuning on 80% of the training data with different hyperparameters and submitted predictions of the two best performing resulting models. We found that this approach worked best for subtask 3, for which we achieved an F1-score of 0.736.