24 citations · 51 across the 12 of their papers we have counts for
7 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…
Interplay: Training Independent Simulators for Reference-Free Conversational Recommendation
Jerome Ramos, Feng Xia, Xi Wang +4
Training conversational recommender systems (CRS) requires extensive dialogue data, which is challenging to collect at scale. To address this, researchers have used simulated user-…