26 citations · 53 across the 9 of their papers we have counts for
4 papers · 2 filters
Generative Retrieval with Few-shot Indexing
Arian Askari, Chuan Meng, Mohammad Aliannejadi +3
Existing generative retrieval (GR) methods rely on training-based indexing, which fine-tunes a model to memorise associations between queries and the document identifiers (docids)…
Can We Use Large Language Models to Fill Relevance Judgment Holes?
Zahra Abbasiantaeb, Chuan Meng, Leif Azzopardi +1
Incomplete relevance judgments limit the re-usability of test collections. When new systems are compared against previous systems used to build the pool of judged documents, they o…
Ranked List Truncation for Large Language Model-based Re-Ranking
Chuan Meng, Negar Arabzadeh, Arian Askari +2
We study ranked list truncation (RLT) from a novel "retrieve-then-re-rank" perspective, where we optimize re-ranking by truncating the retrieved list (i.e., trim re-ranking candida…
Query Performance Prediction using Relevance Judgments Generated by Large Language Models
Chuan Meng, Negar Arabzadeh, Arian Askari +2
Query performance prediction (QPP) aims to estimate the retrieval quality of a search system for a query without human relevance judgments. Previous QPP methods typically return a…