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

collaborators
Showing 2023Show all

8 papers · 1 filter

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.CL2023

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…

cs.CL2023★ 22 cited

The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments

Nailia Mirzakhmedova, Johannes Kiesel, Milad Alshomary +11

We present the Touché23-ValueEval Dataset for Identifying Human Values behind Arguments. To investigate approaches for the automated detection of human values behind arguments, we…