4 papers · 1 filter
RAQG-QPP: Query Performance Prediction with Retrieved Query Variants and Retrieval Augmented Query Generation
Fangzheng Tian, Debasis Ganguly, Craig Macdonald
Query Performance Prediction (QPP) estimates the retrieval quality of ranking models without the use of any human-assessed relevance judgements, and finds applications in query-spe…
Predicting Retrieval Utility and Answer Quality in Retrieval-Augmented Generation
Fangzheng Tian, Debasis Ganguly, Craig Macdonald
The quality of answers generated by large language models (LLMs) in retrieval-augmented generation (RAG) is largely influenced by the contextual information contained in the retrie…
Revisiting Query Variants: The Advantage of Retrieval Over Generation of Query Variants for Effective QPP
Fangzheng Tian, Debasis Ganguly, Craig Macdonald
Leveraging query variants (QVs), i.e., queries with potentially similar information needs to the target query, has been shown to improve the effectiveness of query performance pred…
Neural Passage Quality Estimation for Static Pruning
Xuejun Chang, Debabrata Mishra, Craig Macdonald +1
Neural networks -- especially those that use large, pre-trained language models -- have improved search engines in various ways. Most prominently, they can estimate the relevance o…