7 citations · 12 across the 16 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
It's High Time: A Survey of Temporal Question Answering
Bhawna Piryani, Abdelrahman Abdallah, Jamshid Mozafari +2
Time plays a critical role in how information is generated, retrieved, and interpreted. In this survey, we provide a comprehensive overview of Temporal Question Answering (TQA), a…
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 (…
A Study into Investigating Temporal Robustness of LLMs
Jonas Wallat, Abdelrahman Abdallah, Adam Jatowt +1
Large Language Models (LLMs) encapsulate a surprising amount of factual world knowledge. However, their performance on temporal questions and historical knowledge is limited becaus…
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,…