7 citations · 12 across the 16 of their papers we have counts for
12 papers · 1 filter
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…
On Listwise Reranking for Corpus Feedback
Soyoung Yoon, Jongho Kim, Daeyong Kwon +2
Reranker improves retrieval performance by capturing document interactions. At one extreme, graph-aware adaptive retrieval (GAR) represents an information-rich regime, requiring a…
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 (…
TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questions
Abdelrahman Abdallah, Bhawna Piryani, Jonas Wallat +2
Temporal awareness is crucial in many information retrieval tasks, particularly in scenarios where the relevance of documents depends on their alignment with the query's temporal c…
Guiding Retrieval using LLM-based Listwise Rankers
Mandeep Rathee, Sean MacAvaney, Avishek Anand
Large Language Models (LLMs) have shown strong promise as rerankers, especially in ``listwise'' settings where an LLM is prompted to rerank several search results at once. However,…
Quam: Adaptive Retrieval through Query Affinity Modelling
Mandeep Rathee, Sean MacAvaney, Avishek Anand
Building relevance models to rank documents based on user information needs is a central task in information retrieval and the NLP community. Beyond the direct ad-hoc search settin…
ir_explain: a Python Library of Explainable IR Methods
Sourav Saha, Harsh Agarwal, V Venktesh +4
While recent advancements in Neural Ranking Models have resulted in significant improvements over traditional statistical retrieval models, it is generally acknowledged that the us…
RankingSHAP -- Listwise Feature Attribution Explanations for Ranking Models
Maria Heuss, Maarten de Rijke, Avishek Anand
While SHAP (SHapley Additive exPlanations) and other feature attribution methods are commonly employed to explain model predictions, their application within information retrieval…
Efficient Neural Ranking using Forward Indexes and Lightweight Encoders
Jurek Leonhardt, Henrik Müller, Koustav Rudra +3
Dual-encoder-based dense retrieval models have become the standard in IR. They employ large Transformer-based language models, which are notoriously inefficient in terms of resourc…