47 citations · 51 across the 7 of their papers we have counts for
7 papers
Single-Turn LLM Reformulation Powered Multi-Stage Hybrid Re-Ranking for Tip-of-the-Tongue Known-Item Retrieval
Debayan Mukhopadhyay, Utshab Kumar Ghosh, Shubham Chatterjee
Retrieving known items from vague descriptions, Tip-of-the-Tongue (ToT) retrieval, remains a significant challenge. We propose using a single call to a generic 8B-parameter LLM for…
DREQ: Document Re-Ranking Using Entity-based Query Understanding
Shubham Chatterjee, Iain Mackie, Jeff Dalton
While entity-oriented neural IR models have advanced significantly, they often overlook a key nuance: the varying degrees of influence individual entities within a document have on…
GRM: Generative Relevance Modeling Using Relevance-Aware Sample Estimation for Document Retrieval
Iain Mackie, Ivan Sekulic, Shubham Chatterjee +2
Recent studies show that Generative Relevance Feedback (GRF), using text generated by Large Language Models (LLMs), can enhance the effectiveness of query expansion. However, LLMs…
Adaptive Latent Entity Expansion for Document Retrieval
Iain Mackie, Shubham Chatterjee, Sean MacAvaney +1
Despite considerable progress in neural relevance ranking techniques, search engines still struggle to process complex queries effectively - both in terms of precision and recall.…
Generative and Pseudo-Relevant Feedback for Sparse, Dense and Learned Sparse Retrieval
Iain Mackie, Shubham Chatterjee, Jeffrey Dalton
Pseudo-relevance feedback (PRF) is a classical approach to address lexical mismatch by enriching the query using first-pass retrieval. Moreover, recent work on generative-relevance…
Generative Relevance Feedback with Large Language Models
Iain Mackie, Shubham Chatterjee, Jeffrey Dalton
Current query expansion models use pseudo-relevance feedback to improve first-pass retrieval effectiveness; however, this fails when the initial results are not relevant. Instead o…
Report on the First HIPstIR Workshop on the Future of Information Retrieval
Laura Dietz, Bhaskar Mitra, Jeremy Pickens +21
The vision of HIPstIR is that early stage information retrieval (IR) researchers get together to develop a future for non-mainstream ideas and research agendas in IR. The first ite…