activity
20202026
most citedBeyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

565 citations · 1k across the 32 of their papers we have counts for

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Showing cs.IRShow all

15 papers · 1 filter

cs.IR2026

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…

cs.IR2025★ 5 cited

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…

cs.IR2024★ 8 cited

Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-Ranking

Ferdinand Schlatt, Maik Fröbe, Harrisen Scells +6

Cross-encoders distilled from large language models (LLMs) are often more effective re-rankers than cross-encoders fine-tuned on manually labeled data. However, distilled models do…

cs.IR2024★ 6 cited

Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders

Ferdinand Schlatt, Maik Fröbe, Harrisen Scells +6

Existing cross-encoder models can be categorized as pointwise, pairwise, or listwise. Pairwise and listwise models allow passage interactions, which typically makes them more effec…

cs.IR2024★ 10 cited

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…

cs.IR2023★ 44 cited

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…

cs.IR2023★ 54 cited

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…

cs.IR2023★ 170 cited

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…

cs.IR2023★ 11 cited

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…

cs.IR2023★ 27 cited

The Infinite Index: Information Retrieval on Generative Text-To-Image Models

Niklas Deckers, Maik Fröbe, Johannes Kiesel +4

Conditional generative models such as DALL-E and Stable Diffusion generate images based on a user-defined text, the prompt. Finding and refining prompts that produce a desired imag…

cs.IR2021

FastWARC: Optimizing Large-Scale Web Archive Analytics

Janek Bevendorff, Martin Potthast, Benno Stein

Web search and other large-scale web data analytics rely on processing archives of web pages stored in a standardized and efficient format. Since its introduction in 2008, the IIPC…

cs.IR2021

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…

cs.IR2021★ 24 cited

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…

cs.IR2020★ 5 cited

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…

cs.IR2020★ 14 cited

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…