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

ORCID iDhttps://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

List by: Type | Date

Jump to: 2024 | 2023
Number of items: 5.

2024

Sinhababu, N., Parry, A., Ganguly, D. , Samanta, D. and Mitra, P. (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, A., MacAvaney, S. and Ganguly, D. (2024) Top-Down Partitioning for Efficient List-Wise Ranking. In: ReNeuIR'24, Washington, DC, USA, 18 July 2024, (Accepted for Publication)

Parry, A., MacAvaney, S. and Ganguly, D. (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, A., Ganguly, D. and Chandra, M. (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, A., Fröbe, M., MacAvaney, S. , Potthast, M. and Hagen, M. (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)

This list was generated on Mon Oct 21 20:09:41 2024 BST.
Jump to: Conference Proceedings
Number of items: 5.

Conference Proceedings

Sinhababu, N., Parry, A., Ganguly, D. , Samanta, D. and Mitra, P. (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, A., MacAvaney, S. and Ganguly, D. (2024) Top-Down Partitioning for Efficient List-Wise Ranking. In: ReNeuIR'24, Washington, DC, USA, 18 July 2024, (Accepted for Publication)

Parry, A., MacAvaney, S. and Ganguly, D. (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, A., Ganguly, D. and Chandra, M. (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, A., Fröbe, M., MacAvaney, S. , Potthast, M. and Hagen, M. (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)

This list was generated on Mon Oct 21 20:09:41 2024 BST.

Supervisors