24 citations · 56 across the 9 of their papers we have counts for
5 papers · 1 filter
Detecting RAG Advertisements Across Advertising Styles
Sebastian Heineking, Wilhelm Pertsch, Ines Zelch +4
Large language models (LLMs) enable a new form of advertising for retrieval-augmented generation (RAG) systems in which organic responses are blended with contextually relevant ads…
The Viability of Crowdsourcing for RAG Evaluation
Lukas Gienapp, Tim Hagen, Maik Fröbe +4
How good are humans at writing and judging responses in retrieval-augmented generation (RAG) scenarios? To answer this question, we investigate the efficacy of crowdsourcing for RA…
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…
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…