24 citations · 51 across the 12 of their papers we have counts for
12 papers · 1 filter
Assessing the Downstream Utility of Evidence-Aware Retrieval in RAG
Utshab Kumar Ghosh, Debayan Mukhopadhyay, Shubham Chatterjee
Retrieval evaluation for retrieval-augmented generation (RAG) is increasingly designed around whether retrieved passages contain evidence that can support generation, rather than t…
When Do Anchor-Based Pointwise LLM Rerankers Help? Retriever Quality, Statistical Scope, and Anchor Design
Utshab Kumar Ghosh, Shubham Chatterjee
Anchor-based pointwise LLM reranking scores each candidate against a shared reference passage to recover cross-document context at pointwise cost. We study when this actually helps…
Bridge Evidence: Static Retrieval Utility Does Not Predict Causal Utility in Multi-Step Agentic Search
Debayan Mukhopadhyay, Utshab Kumar Ghosh, Shubham Chatterjee
Retrieval systems are trained and evaluated on a static idea of usefulness: hand a document and a question to a reader model, see whether the answer improves, and score the documen…
Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Ranking
Utshab Kumar Ghosh, Shubham Chatterjee
Entity-aware document retrieval uses query-associated entities as ranking signals, assuming that semantically relevant entities are also useful retrieval signals. We show this assu…
Reproduction Beyond Benchmarks: ConstBERT and ColBERT-v2 Across Backends and Query Distributions
Utshab Kumar Ghosh, Ashish David, Shubham Chatterjee
Reproducibility must validate architectural robustness, not just numerical accuracy. We evaluate ColBERT-v2 and ConstBERT across five dimensions, finding that while ConstBERT repro…
Entities as Retrieval Signals: A Systematic Study of Coverage, Supervision, and Evaluation in Entity-Oriented Ranking
Shubham Chatterjee
Entity-oriented retrieval assumes that relevant documents exhibit query-relevant entities, yet evaluations report conflicting results. We show this inconsistency stems not from mod…
REGENT: Relevance-Guided Attention for Entity-Aware Multi-Vector Neural Re-Ranking
Shubham Chatterjee
Current neural re-rankers often struggle with complex information needs and long, content-rich documents. The fundamental issue is not computational--it is intelligent content sele…
QDER: Query-Specific Document and Entity Representations for Multi-Vector Document Re-Ranking
Shubham Chatterjee, Jeff Dalton
Neural IR has advanced through two distinct paths: entity-oriented approaches leveraging knowledge graphs and multi-vector models capturing fine-grained semantics. We introduce QDE…
DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities
Thong Nguyen, Shubham Chatterjee, Sean MacAvaney +3
Learned Sparse Retrieval (LSR) models use vocabularies from pre-trained transformers, which often split entities into nonsensical fragments. Splitting entities can reduce retrieval…
Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search
Hideaki Joko, Shubham Chatterjee, Andrew Ramsay +3
The future of conversational agents will provide users with personalized information responses. However, a significant challenge in developing models is the lack of large-scale dia…
TREC iKAT 2023: A Test Collection for Evaluating Conversational and Interactive Knowledge Assistants
Mohammad Aliannejadi, Zahra Abbasiantaeb, Shubham Chatterjee +2
Conversational information seeking has evolved rapidly in the last few years with the development of Large Language Models (LLMs), providing the basis for interpreting and respondi…
TREC iKAT 2023: The Interactive Knowledge Assistance Track Overview
Mohammad Aliannejadi, Zahra Abbasiantaeb, Shubham Chatterjee +2
Conversational Information Seeking has evolved rapidly in the last few years with the development of Large Language Models providing the basis for interpreting and responding in a…