6 citations · 12 across the 20 of their papers we have counts for
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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…
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…
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…
MWPRanker: An Expression Similarity Based Math Word Problem Retriever
Mayank Goel, Venktesh V, Vikram Goyal
Math Word Problems (MWPs) in online assessments help test the ability of the learner to make critical inferences by interpreting the linguistic information in them. To test the mat…
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…
Topic Aware Contextualized Embeddings for High Quality Phrase Extraction
Venktesh V, Mukesh Mohania, Vikram Goyal
Keyphrase extraction from a given document is the task of automatically extracting salient phrases that best describe the document. This paper proposes a novel unsupervised graph-b…