12 citations · 19 across the 19 of their papers we have counts for
6 papers · 1 filter
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…
"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…
Evaluating the Explainability of Neural Rankers
Saran Pandian, Debasis Ganguly, Sean MacAvaney
Information retrieval models have witnessed a paradigm shift from unsupervised statistical approaches to feature-based supervised approaches to completely data-driven ones that mak…
A Deep Learning Approach for Selective Relevance Feedback
Suchana Datta, Debasis Ganguly, Sean MacAvaney +1
Pseudo-relevance feedback (PRF) can enhance average retrieval effectiveness over a sufficiently large number of queries. However, PRF often introduces a drift into the original inf…