Query Performance Prediction (QPP) is currently primarily used for ad-hoc retrieval tasks. The Information Retrieval (IR) field is reaching new heights thanks to recent advances in large language models and neural networks, as well as emerging new ways of searching, such as conversational search. Such advancements are quickly spreading to adjacent research areas, including QPP, necessitating reconsidering how we perform and evaluate QPP.Important Dates
Submission deadline: February 12th, 2023
Notification of acceptance: March 5th, 2023
Camera ready: March 15th, 2023
Workshop day: April 2nd, 2023
Conference days: April 3rd-6th, 2023
Call for Papers
This workshop aims at stimulating discussion on three main aspects concerning the future of
What are the emerging QPP challenges posed by new methods and technologies, including but not limited to dense retrieval, contextualized embeddings, and conversational search?
How might these new techniques be used to improve the quality of QPP?
Can we claim that the current techniques for evaluating QPP are effective in all arising scenarios? Can we envision new evaluation protocols capable of granting generalizability in new domains?
We plan to foster the discussion via two focus groups led by the workshop's organizers.
The first focus group will identify what possibilities the QPP offers regarding new research models and IR tasks, primary considerations, issues linked to different aspects of the QPP, and the potentialities provided by new tools.
The second focus group will gather the community’s concerns and solutions with respect to the QPP evaluation, especially for what concerns emerging domains.
The workshop will focus on the following themes:
Query performance prediction applied to new tasks:
Can existing QPP techniques be exploited, or which new QPP theories and models need to be devised for new tasks, such as passage-retrieval, Q&A, and conversational search?
Query performance prediction exploiting new techniques:
How can new technologies like contextualized embeddings, large language models, and neural networks be exploited to improve QPP?
Evaluation of query performance prediction:
How should QPP techniques be evaluated, including best practices, datasets, and resources, and, in particular, should QPP be evaluated the same for different IR tasks?
It is possible to submit three main categories of manuscripts to the workshop:
Full papers: up to 6 pages.
Short papers: up to 3 pages.
Discussion papers: up to 3 pages.
All manuscripts are expected to address the workshop's themes as mentioned above. Full and short papers should contain innovative ideas and their experimental evaluation. We are also interested in works containing (methodologically sound) preliminary results and incremental endeavours.
Discussion papers should include work with or without preliminary results, position papers, and papers describing failures. Such papers should foster the discussion and thus are not required to contain full-fledged results. In this sense, the experimental evaluation of the submitted discussion paper is appreciated but not required.
We are also interested in receiving contributions regarding (methodologically sound) failed experiments; since the workshop will focus on new research directions, we consider it necessary also to discuss the reasons and causes of failures.
Each manuscript will be peer-reviewed by at least two program committee members.
Accepted papers will be published online as a volume of the CEUR-WS proceeding series.
Submit your contribution via Easychair at the following link
To prepare the submission, use the one-column CEUR template. A precompiled version is
Guglielmo Faggioli, University of Padova, Italy, fagg...@dei.unipd.it
Nicola Ferro, University of Padova, Italy, fe...@dei.unipd.it
Josiane Mothe, Université de Toulouse, IRIT, France, josian...@irit.fr
Fiana Raiber, Yahoo Research, Israel, fi...@yahooinc.com