7 papers
When More Reformulations Hurt: Avoiding Drift using Ranker Feedback
V Venktesh, Mandeep Rathee, Avishek Anand
Modern retrieval pipelines increasingly rely on query reformulation and neural reranking to improve effectiveness, but this comes at a significant computational cost and introduces…
Reproducing Adaptive Reranking for Reasoning-Intensive IR
Mandeep Rathee, V Venktesh, Sean MacAvaney +1
The classical cascading pipeline of retrieve--rerank suffers from a bounded recall problem, stemming from limitations of the first-stage retriever. Most current approaches address…
Trust but Verify! A Survey on Verification Design for Test-time Scaling
V Venktesh, Mandeep Rathee, Avishek Anand
Test-time scaling (TTS) has emerged as a new frontier for scaling the performance of Large Language Models. In test-time scaling, by using more computational resources during infer…
Test-time Corpus Feedback: From Retrieval to RAG
Mandeep Rathee, V Venktesh, Sean MacAvaney +1
Retrieval-Augmented Generation (RAG) has emerged as a standard framework for knowledge-intensive NLP tasks, combining large language models (LLMs) with document retrieval from exte…
Breaking the Lens of the Telescope: Online Relevance Estimation over Large Retrieval Sets
Mandeep Rathee, V Venktesh, Sean MacAvaney +1
Advanced relevance models, such as those that use large language models (LLMs), provide highly accurate relevance estimations. However, their computational costs make them infeasib…
SUNAR: Semantic Uncertainty based Neighborhood Aware Retrieval for Complex QA
V Venktesh, Mandeep Rathee, Avishek Anand
Complex question-answering (QA) systems face significant challenges in retrieving and reasoning over information that addresses multi-faceted queries. While large language models (…