12 citations · 19 across the 19 of their papers we have counts for
7 papers · 1 filter
Revisiting Query Variants: The Advantage of Retrieval Over Generation of Query Variants for Effective QPP
Fangzheng Tian, Debasis Ganguly, Craig Macdonald
Leveraging query variants (QVs), i.e., queries with potentially similar information needs to the target query, has been shown to improve the effectiveness of query performance pred…
T-Retrievability: A Topic-Focused Approach to Measure Fair Document Exposure in Information Retrieval
Xuejun Chang, Zaiqiao Meng, Debasis Ganguly
Retrievability of a document is a collection-based statistic that measures its expected (reciprocal) rank of being retrieved within a specific rank cut-off. A collection with unifo…
Am I on the Right Track? What Can Predicted Query Performance Tell Us about the Search Behaviour of Agentic RAG
Fangzheng Tian, Jinyuan Fang, Debasis Ganguly +2
Agentic Retrieval-Augmented Generation (RAG) is a new paradigm where the reasoning model decides when to invoke a retriever (as a "tool") when answering a question. This paradigm,…
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…
Exploring the Role of Diversity in Example Selection for In-Context Learning
Janak Kapuriya, Manit Kaushik, Debasis Ganguly +1
In-Context Learning (ICL) has gained prominence due to its ability to perform tasks without requiring extensive training data and its robustness to noisy labels. A typical ICL work…
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…
Is Relevance Propagated from Retriever to Generator in RAG?
Fangzheng Tian, Debasis Ganguly, Craig Macdonald
Retrieval Augmented Generation (RAG) is a framework for incorporating external knowledge, usually in the form of a set of documents retrieved from a collection, as a part of a prom…