2 citations · 3 across the 9 of their papers we have counts for
9 papers
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…
Think Right, Not More: Test-Time Scaling for Numerical Claim Verification
Primakov Chungkham, V Venktesh, Vinay Setty +1
Fact-checking real-world claims, particularly numerical claims, is inherently complex that require multistep reasoning and numerical reasoning for verifying diverse aspects of the…
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…
Evaluating List Construction and Temporal Understanding capabilities of Large Language Models
Alexandru Dumitru, V Venktesh, Adam Jatowt +1
Large Language Models (LLMs) have demonstrated immense advances in a wide range of natural language tasks. However, these models are susceptible to hallucinations and errors on par…
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…
FactIR: A Real-World Zero-shot Open-Domain Retrieval Benchmark for Fact-Checking
Venktesh V, Vinay Setty
The field of automated fact-checking increasingly depends on retrieving web-based evidence to determine the veracity of claims in real-world scenarios. A significant challenge in t…
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…
The Surprising Effectiveness of Rankers Trained on Expanded Queries
Abhijit Anand, Venktesh V, Vinay Setty +1
An important problem in text-ranking systems is handling the hard queries that form the tail end of the query distribution. The difficulty may arise due to the presence of uncommon…
TagRec++: Hierarchical Label Aware Attention Network for Question Categorization
Venktesh Viswanathan, Mukesh Mohania, Vikram Goyal
Online learning systems have multiple data repositories in the form of transcripts, books and questions. To enable ease of access, such systems organize the content according to a…