Andrew Parry
Email: a.parry.1@research.gla.ac.uk
Website: parry-parry.github.io
Office:
Room 502, Level 5
Sir Alwyn Williams Building
School of Computing Science
Glasgow, G12 8QQ
https://orcid.org/0000-0001-5446-8328
Research title: Modelling Uncertainties in Neural Networks for Improved Defence against Adversarial Attacks
Research Summary
I obtained my BSc in Computing Science from the University of Glasgow in 2022. After a research placement I joined the IR group at Glasgow working on search. I have made contributions to the PyTerrier framework maintained at Glasgow which can be accessed from my website.
Main Research Interests
- Neural Ranking Models
- Adversarial Attacks in Search
- Efficient Neural Training
My Research
I am interested in how the overparameterized language models that underpin neural search often display behaviour which diverges from a user's notion of relevance. I am working to expose these weaknesses whilst improving efficiency to make neural search a more viable option in industry.
Publications
2025
Fröbe, Maik, Parry, Andrew, Schlatt, Ferdinand, MacAvaney, Sean 
ORCID: https://orcid.org/0000-0002-8914-2659, Stein, Benno, Potthast, Martin and Hagen, Matthias
(2025)
Large Language Model Relevance Assessors Agree With One Another More Than With Human Assessors.
In: 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2025), Padua, Italy, 13-17 July 2025,
(Accepted for Publication)
Parry, Andrew, Fröbe, Maik, Scells, Harrisen, Schlatt, Ferdinand, Faggioli, Guglielmo, Zerhoudi, Saber, MacAvaney, Sean ORCID: https://orcid.org/0000-0002-8914-2659 and Yang, Eugene
(2025)
Variations in Relevance Judgments and the Shelf Life of Test Collections.
In: 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2025), Padua, Italy, 13-17 July 2025,
(Accepted for Publication)
2024
Sinhababu, Nilanjan, Parry, Andrew, Ganguly, Debasis ORCID: https://orcid.org/0000-0003-0050-7138, Samanta, Debasis and Mitra, Pabitra
(2024)
Few-shot Pairwise Ranking Prompting: An Effective Non-Parametric Retrieval Model.
In: 2024 Conference on Empirical Methods in Natural Language Processing, Miami, Florida, USA, 12–16 November 2024,
(Accepted for Publication)
Parry, Andrew, MacAvaney, Sean ORCID: https://orcid.org/0000-0002-8914-2659 and Ganguly, Debasis ORCID: https://orcid.org/0000-0003-0050-7138
(2024)
Top-Down Partitioning for Efficient List-Wise Ranking.
ReNeuIR'24, Washington, DC, USA, 18 July 2024.
(Accepted for Publication)
Parry, Andrew, MacAvaney, Sean ORCID: https://orcid.org/0000-0002-8914-2659 and Ganguly, Debasis ORCID: https://orcid.org/0000-0003-0050-7138
(2024)
Exploiting Positional Bias for Query-Agnostic Generative Content in Search.
In: 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), Bangkok, Thailand, 11-16 August 2024,
(Accepted for Publication)
Parry, Andrew, Ganguly, Debasis ORCID: https://orcid.org/0000-0003-0050-7138 and Chandra, Manish
(2024)
In-Context Learning or: How I Learned to Stop Worrying and Love Applied Information Retrieval.
In: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024), Washington D.C., USA, 14-18 Jul 2024,
(Accepted for Publication)
2023
Parry, Andrew, Fröbe, Mark, MacAvaney, Sean ORCID: https://orcid.org/0000-0002-8914-2659, Potthast, Martin and Hagen, Matthias
(2023)
Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models.
In: 46th European Conference on Information Retrieval (ECIR2024), Glasgow, Scotland, 24-28 March 2024,
(Accepted for Publication)
Conference or Workshop Item
Parry, Andrew, MacAvaney, Sean ORCID: https://orcid.org/0000-0002-8914-2659 and Ganguly, Debasis ORCID: https://orcid.org/0000-0003-0050-7138
(2024)
Top-Down Partitioning for Efficient List-Wise Ranking.
ReNeuIR'24, Washington, DC, USA, 18 July 2024.
(Accepted for Publication)
Conference Proceedings
Fröbe, Maik, Parry, Andrew, Schlatt, Ferdinand, MacAvaney, Sean ORCID: https://orcid.org/0000-0002-8914-2659, Stein, Benno, Potthast, Martin and Hagen, Matthias
(2025)
Large Language Model Relevance Assessors Agree With One Another More Than With Human Assessors.
In: 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2025), Padua, Italy, 13-17 July 2025,
(Accepted for Publication)
Parry, Andrew, Fröbe, Maik, Scells, Harrisen, Schlatt, Ferdinand, Faggioli, Guglielmo, Zerhoudi, Saber, MacAvaney, Sean ORCID: https://orcid.org/0000-0002-8914-2659 and Yang, Eugene
(2025)
Variations in Relevance Judgments and the Shelf Life of Test Collections.
In: 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2025), Padua, Italy, 13-17 July 2025,
(Accepted for Publication)
Sinhababu, Nilanjan, Parry, Andrew, Ganguly, Debasis ORCID: https://orcid.org/0000-0003-0050-7138, Samanta, Debasis and Mitra, Pabitra
(2024)
Few-shot Pairwise Ranking Prompting: An Effective Non-Parametric Retrieval Model.
In: 2024 Conference on Empirical Methods in Natural Language Processing, Miami, Florida, USA, 12–16 November 2024,
(Accepted for Publication)
Parry, Andrew, MacAvaney, Sean ORCID: https://orcid.org/0000-0002-8914-2659 and Ganguly, Debasis ORCID: https://orcid.org/0000-0003-0050-7138
(2024)
Exploiting Positional Bias for Query-Agnostic Generative Content in Search.
In: 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), Bangkok, Thailand, 11-16 August 2024,
(Accepted for Publication)
Parry, Andrew, Ganguly, Debasis ORCID: https://orcid.org/0000-0003-0050-7138 and Chandra, Manish
(2024)
In-Context Learning or: How I Learned to Stop Worrying and Love Applied Information Retrieval.
In: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2024), Washington D.C., USA, 14-18 Jul 2024,
(Accepted for Publication)
Parry, Andrew, Fröbe, Mark, MacAvaney, Sean ORCID: https://orcid.org/0000-0002-8914-2659, Potthast, Martin and Hagen, Matthias
(2023)
Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models.
In: 46th European Conference on Information Retrieval (ECIR2024), Glasgow, Scotland, 24-28 March 2024,
(Accepted for Publication)
