12 citations · 25 across the 12 of their papers we have counts for
12 papers
SuiteEval: Simplifying Retrieval Benchmarks
Andrew Parry, Debasis Ganguly, Sean MacAvaney
Information retrieval evaluation often suffers from fragmented practices -- varying dataset subsets, aggregation methods, and pipeline configurations -- that undermine reproducibil…
Disentangling Locality and Entropy in Ranking Distillation
Andrew Parry, Debasis Ganguly, Sean MacAvaney
The training process of ranking models involves two key data selection decisions: a sampling strategy, and a labeling strategy. Modern ranking systems, especially those for perform…
Modeling Ranking Properties with In-Context Learning
Nilanjan Sinhababu, Andrew Parry, Debasis Ganguly +1
While standard IR models are primarily designed to optimize relevance, real-world search often needs to balance additional objectives such as diversity and fairness. These objectiv…
Variations in Relevance Judgments and the Shelf Life of Test Collections
Andrew Parry, Maik Fröbe, Harrisen Scells +5
The fundamental property of Cranfield-style evaluations, that system rankings are stable even when assessors disagree on individual relevance decisions, was validated on traditiona…
MechIR: A Mechanistic Interpretability Framework for Information Retrieval
Andrew Parry, Catherine Chen, Carsten Eickhoff +1
Mechanistic interpretability is an emerging diagnostic approach for neural models that has gained traction in broader natural language processing domains. This paradigm aims to pro…
Training on the Test Model: Contamination in Ranking Distillation
Vishakha Suresh Kalal, Andrew Parry, Sean MacAvaney
Neural approaches to ranking based on pre-trained language models are highly effective in ad-hoc search. However, the computational expense of these models can limit their applicat…
Few-shot Prompting for Pairwise Ranking: An Effective Non-Parametric Retrieval Model
Nilanjan Sinhababu, Andrew Parry, Debasis Ganguly +2
A supervised ranking model, despite its advantage of being effective, usually involves complex processing - typically multiple stages of task-specific pre-training and fine-tuning.…
Top-Down Partitioning for Efficient List-Wise Ranking
Andrew Parry, Sean MacAvaney, Debasis Ganguly
Large Language Models (LLMs) have significantly impacted many facets of natural language processing and information retrieval. Unlike previous encoder-based approaches, the enlarge…
Generative Relevance Feedback and Convergence of Adaptive Re-Ranking: University of Glasgow Terrier Team at TREC DL 2023
Andrew Parry, Thomas Jaenich, Sean MacAvaney +1
This paper describes our participation in the TREC 2023 Deep Learning Track. We submitted runs that apply generative relevance feedback from a large language model in both a zero-s…
"In-Context Learning" or: How I learned to stop worrying and love "Applied Information Retrieval"
Andrew Parry, Debasis Ganguly, Manish Chandra
With the increasing ability of large language models (LLMs), in-context learning (ICL) has evolved as a new paradigm for natural language processing (NLP), where instead of fine-tu…
Exploiting Positional Bias for Query-Agnostic Generative Content in Search
Andrew Parry, Sean MacAvaney, Debasis Ganguly
In recent years, neural ranking models (NRMs) have been shown to substantially outperform their lexical counterparts in text retrieval. In traditional search pipelines, a combinati…
Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models
Andrew Parry, Maik Fröbe, Sean MacAvaney +2
Modern sequence-to-sequence relevance models like monoT5 can effectively capture complex textual interactions between queries and documents through cross-encoding. However, the use…