15 citations · 22 across the 9 of their papers we have counts for
9 papers
Understanding the User: An Intent-Based Ranking Dataset
Abhijit Anand, Jurek Leonhardt, V Venktesh +1
As information retrieval systems continue to evolve, accurate evaluation and benchmarking of these systems become pivotal. Web search datasets, such as MS MARCO, primarily provide…
QuanTemp: A real-world open-domain benchmark for fact-checking numerical claims
Venktesh V, Abhijit Anand, Avishek Anand +1
Automated fact checking has gained immense interest to tackle the growing misinformation in the digital era. Existing systems primarily focus on synthetic claims on Wikipedia, and…
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…
Data Augmentation for Sample Efficient and Robust Document Ranking
Abhijit Anand, Jurek Leonhardt, Jaspreet Singh +2
Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models ten…
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…
Context Aware Query Rewriting for Text Rankers using LLM
Abhijit Anand, Venktesh V, Vinay Setty +1
Query rewriting refers to an established family of approaches that are applied to underspecified and ambiguous queries to overcome the vocabulary mismatch problem in document ranki…
Query Understanding in the Age of Large Language Models
Avishek Anand, Venktesh V, Abhijit Anand +1
Querying, conversing, and controlling search and information-seeking interfaces using natural language are fast becoming ubiquitous with the rise and adoption of large-language mod…
Supervised Contrastive Learning Approach for Contextual Ranking
Abhijit Anand, Jurek Leonhardt, Koustav Rudra +1
Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models ten…
Efficient Neural Ranking using Forward Indexes
Jurek Leonhardt, Koustav Rudra, Megha Khosla +2
Neural document ranking approaches, specifically transformer models, have achieved impressive gains in ranking performance. However, query processing using such over-parameterized…