25 citations · 60 across the 27 of their papers we have counts for
27 papers
Towards a Relevance Posterior in Neural Information Access
Andrew Parry, Emmanouil Georgios Lionis, Debasis Ganguly +1
Modern information retrieval systems typically operationalise relevance as a query-conditional score computed at inference time. This design choice has become dominant such that al…
The Matryoshka Hypencoder
Majd Alkawaas, Sean MacAvaney
The Hypencoder is a recently-proposed retrieval approach that encodes queries as shallow neural networks ("Q-Nets") that estimate relevance over pre-computed document embeddings. I…
A Picture of Agentic Search
Francesca Pezzuti, Ophir Frieder, Fabrizio Silvestri +2
With automated systems increasingly issuing search queries alongside humans, Information Retrieval (IR) faces a major shift. Yet IR remains human-centred, with systems, evaluation…
NeuCLIRTech: Chinese Monolingual and Cross-Language Information Retrieval Evaluation in a Challenging Domain
Dawn Lawrie, James Mayfield, Eugene Yang +6
Measuring advances in retrieval requires test collections with relevance judgments that can faithfully distinguish systems. This paper presents NeuCLIRTech, an evaluation collectio…
Overview of the TREC 2025 RAGTIME Track
Dawn Lawrie, Sean MacAvaney, James Mayfield +3
The principal goal of the RAG TREC Instrument for Multilingual Evaluation (RAGTIME) track at TREC is to study report generation from multilingual source documents. The track has cr…
NeuCLIRBench: A Modern Evaluation Collection for Monolingual, Cross-Language, and Multilingual Information Retrieval
Dawn Lawrie, James Mayfield, Eugene Yang +6
To measure advances in retrieval, test collections with relevance judgments that can faithfully distinguish systems are required. This paper presents NeuCLIRBench, an evaluation co…
Overview of the TREC 2024 NeuCLIR Track
Dawn Lawrie, Sean MacAvaney, James Mayfield +4
The principal goal of the TREC Neural Cross-Language Information Retrieval (NeuCLIR) track is to study the effect of neural approaches on cross-language information access. The tra…
Neural Prioritisation for Web Crawling
Francesca Pezzuti, Sean MacAvaney, Nicola Tonellotto
Given the vast scale of the Web, crawling prioritisation techniques based on link graph traversal, popularity, link analysis, and textual content are frequently applied to surface…
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…
An Alternative to FLOPS Regularization to Effectively Productionize SPLADE-Doc
Aldo Porco, Dhruv Mehra, Igor Malioutov +5
Learned Sparse Retrieval (LSR) models encode text as weighted term vectors, which need to be sparse to leverage inverted index structures during retrieval. SPLADE, the most popular…
Breaking the Lens of the Telescope: Online Relevance Estimation over Large Retrieval Sets
Mandeep Rathee, V Venktesh, Sean MacAvaney +1
Advanced relevance models, such as those that use large language models (LLMs), provide highly accurate relevance estimations. However, their computational costs make them infeasib…
Document Quality Scoring for Web Crawling
Francesca Pezzuti, Ariane Mueller, Sean MacAvaney +1
The internet contains large amounts of low-quality content, yet users expect web search engines to deliver high-quality, relevant results. The abundant presence of low-quality page…
MURR: Model Updating with Regularized Replay for Searching a Document Stream
Eugene Yang, Nicola Tonellotto, Dawn Lawrie +4
The Internet produces a continuous stream of new documents and user-generated queries. These naturally change over time based on events in the world and the evolution of language.…
Efficient Constant-Space Multi-Vector Retrieval
Sean MacAvaney, Antonio Mallia, Nicola Tonellotto
Multi-vector retrieval methods, exemplified by the ColBERT architecture, have shown substantial promise for retrieval by providing strong trade-offs in terms of retrieval latency a…
Exploring the Effectiveness of Multi-stage Fine-tuning for Cross-encoder Re-rankers
Francesca Pezzuti, Sean MacAvaney, Nicola Tonellotto
State-of-the-art cross-encoders can be fine-tuned to be highly effective in passage re-ranking. The typical fine-tuning process of cross-encoders as re-rankers requires large amoun…
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…
mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval
Orion Weller, Benjamin Chang, Eugene Yang +7
Retrieval systems generally focus on web-style queries that are short and underspecified. However, advances in language models have facilitated the nascent rise of retrieval models…
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…
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…
On the Evaluation of Machine-Generated Reports
James Mayfield, Eugene Yang, Dawn Lawrie +10
Large Language Models (LLMs) have enabled new ways to satisfy information needs. Although great strides have been made in applying them to settings like document ranking and short-…
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…
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…
A Reproducibility Study of PLAID
Sean MacAvaney, Nicola Tonellotto
The PLAID (Performance-optimized Late Interaction Driver) algorithm for ColBERTv2 uses clustered term representations to retrieve and progressively prune documents for final (exact…