170 citations · 387 across the 17 of their papers we have counts for
19 papers
When the Music Stops: Tip-of-the-Tongue Retrieval for Music
Samarth Bhargav, Anne Schuth, Claudia Hauff
We present a study of Tip-of-the-tongue (ToT) retrieval for music, where a searcher is trying to find an existing music entity, but is unable to succeed as they cannot accurately r…
Hear Me Out: A Study on the Use of the Voice Modality for Crowdsourced Relevance Assessments
Nirmal Roy, Agathe Balayn, David Maxwell +1
The creation of relevance assessments by human assessors (often nowadays crowdworkers) is a vital step when building IR test collections. Prior works have investigated assessor qua…
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…
Do the Findings of Document and Passage Retrieval Generalize to the Retrieval of Responses for Dialogues?
Gustavo Penha, Claudia Hauff
A number of learned sparse and dense retrieval approaches have recently been proposed and proven effective in tasks such as passage retrieval and document retrieval. In this paper…
Users and Contemporary SERPs: A (Re-)Investigation Examining User Interactions and Experiences
Nirmal Roy, David Maxwell, Claudia Hauff
The Search Engine Results Page (SERP) has evolved significantly over the last two decades, moving away from the simple ten blue links paradigm to considerably more complex presenta…
Moving Stuff Around: A study on efficiency of moving documents into memory for Neural IR models
Arthur Câmara, Claudia Hauff
When training neural rankers using Large Language Models, it's expected that a practitioner would make use of multiple GPUs to accelerate the training time. By using more devices,…