26 citations · 41 across the 6 of their papers we have counts for
8 papers · 1 filter
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)…
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…
Answer Retrieval in Legal Community Question Answering
Arian Askari, Zihui Yang, Zhaochun Ren +1
The task of answer retrieval in the legal domain aims to help users to seek relevant legal advice from massive amounts of professional responses. Two main challenges hinder applyin…
On the Interpolation of Contextualized Term-based Ranking with BM25 for Query-by-Example Retrieval
Amin Abolghasemi, Arian Askari, Suzan Verberne
Term-based ranking with pre-trained transformer-based language models has recently gained attention as they bring the contextualization power of transformer models into the highly…
LeiBi@COLIEE 2022: Aggregating Tuned Lexical Models with a Cluster-driven BERT-based Model for Case Law Retrieval
Arian Askari, Georgios Peikos, Gabriella Pasi +1
This paper summarizes our approaches submitted to the case law retrieval task in the Competition on Legal Information Extraction/Entailment (COLIEE) 2022. Our methodology consists…