565 citations · 979 across the 23 of their papers we have counts for
23 papers
Detecting Generated Native Ads in Conversational Search
Sebastian Schmidt, Ines Zelch, Janek Bevendorff +3
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…
Evaluating Generative Ad Hoc Information Retrieval
Lukas Gienapp, Harrisen Scells, Niklas Deckers +9
Recent advances in large language models have enabled the development of viable generative retrieval systems. Instead of a traditional document ranking, generative retrieval system…
Commercialized Generative AI: A Critical Study of the Feasibility and Ethics of Generating Native Advertising Using Large Language Models in Conversational Web Search
Ines Zelch, Matthias Hagen, Martin Potthast
How will generative AI pay for itself? Unless charging users for access, selling advertising is the only alternative. Especially in the multi-billion dollar web search market with…
The Information Retrieval Experiment Platform
Maik Fröbe, Jan Heinrich Reimer, Sean MacAvaney +6
We integrate ir_datasets, ir_measures, and PyTerrier with TIRA in the Information Retrieval Experiment Platform (TIREx) to promote more standardized, reproducible, scalable, and ev…
Perspectives on Large Language Models for Relevance Judgment
Guglielmo Faggioli, Laura Dietz, Charles Clarke +8
When asked, large language models (LLMs) like ChatGPT claim that they can assist with relevance judgments but it is not clear whether automated judgments can reliably be used in ev…
The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web Archives
Jan Heinrich Reimer, Sebastian Schmidt, Maik Fröbe +5
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…
Paraphrase Acquisition from Image Captions
Marcel Gohsen, Matthias Hagen, Martin Potthast +1
We propose to use image captions from the Web as a previously underutilized resource for paraphrases (i.e., texts with the same "message") and to create and analyze a corresponding…
Sparse Pairwise Re-ranking with Pre-trained Transformers
Lukas Gienapp, Maik Fröbe, Matthias Hagen +1
Pairwise re-ranking models predict which of two documents is more relevant to a query and then aggregate a final ranking from such preferences. This is often more effective than po…
How Train-Test Leakage Affects Zero-shot Retrieval
Maik Fröbe, Christopher Akiki, Martin Potthast +1
Neural retrieval models are often trained on (subsets of) the millions of queries of the MS MARCO / ORCAS datasets and then tested on the 250 Robust04 queries or other TREC benchma…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Clickbait Spoiling via Question Answering and Passage Retrieval
Matthias Hagen, Maik Fröbe, Artur Jurk +1
We introduce and study the task of clickbait spoiling: generating a short text that satisfies the curiosity induced by a clickbait post. Clickbait links to a web page and advertise…
The Impact of Main Content Extraction on Near-Duplicate Detection
Maik Fröbe, Matthias Hagen, Janek Bevendorff +6
Commercial web search engines employ near-duplicate detection to ensure that users see each relevant result only once, albeit the underlying web crawls typically include (near-)dup…
Towards Axiomatic Explanations for Neural Ranking Models
Michael Völske, Alexander Bondarenko, Maik Fröbe +4
Recently, neural networks have been successfully employed to improve upon state-of-the-art performance in ad-hoc retrieval tasks via machine-learned ranking functions. While neural…
Web Archive Analytics
Michael Völske, Janek Bevendorff, Johannes Kiesel +4
Web archive analytics is the exploitation of publicly accessible web pages and their evolution for research purposes -- to the extent organizationally possible for researchers. In…
Query Interpretations from Entity-Linked Segmentations
Vaibhav Kasturia, Marcel Gohsen, Matthias Hagen
Web search queries can be ambiguous: is "source of the nile" meant to find information on the actual river or on a board game of that name? We tackle this problem by deriving entit…
The Importance of Suppressing Domain Style in Authorship Analysis
Sebastian Bischoff, Niklas Deckers, Marcel Schliebs +5
The prerequisite of many approaches to authorship analysis is a representation of writing style. But despite decades of research, it still remains unclear to what extent commonly u…
Conversational Search -- A Report from Dagstuhl Seminar 19461
Avishek Anand, Lawrence Cavedon, Matthias Hagen +3
Dagstuhl Seminar 19461 "Conversational Search" was held on 10-15 November 2019. 44~researchers in Information Retrieval and Web Search, Natural Language Processing, Human Computer…
Abstractive Snippet Generation
Wei-Fan Chen, Shahbaz Syed, Benno Stein +2
An abstractive snippet is an originally created piece of text to summarize a web page on a search engine results page. Compared to the conventional extractive snippets, which are g…
Common Conversational Community Prototype: Scholarly Conversational Assistant
Krisztian Balog, Lucie Flekova, Matthias Hagen +6
This paper discusses the potential for creating academic resources (tools, data, and evaluation approaches) to support research in conversational search, by focusing on realistic i…
Answering Comparative Questions: Better than Ten-Blue-Links?
Matthias Schildwächter, Alexander Bondarenko, Julian Zenker +3
We present CAM (comparative argumentative machine), a novel open-domain IR system to argumentatively compare objects with respect to information extracted from the Common Crawl. In…
The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength
Martin Potthast, Tim Gollub, Matthias Hagen +1
Clickbait has grown to become a nuisance to social media users and social media operators alike. Malicious content publishers misuse social media to manipulate as many users as pos…
Wikipedia Text Reuse: Within and Without
Milad Alshomary, Michael Völske, Tristan Licht +4
We study text reuse related to Wikipedia at scale by compiling the first corpus of text reuse cases within Wikipedia as well as without (i.e., reuse of Wikipedia text in a sample o…
Heuristic Feature Selection for Clickbait Detection
Matti Wiegmann, Michael Völske, Benno Stein +2
We study feature selection as a means to optimize the baseline clickbait detector employed at the Clickbait Challenge 2017. The challenge's task is to score the "clickbaitiness" of…