26 citations · 33 across the 5 of their papers we have counts for
5 papers
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…
Measuring Bias in a Ranked List using Term-based Representations
Amin Abolghasemi, Leif Azzopardi, Arian Askari +2
In most recent studies, gender bias in document ranking is evaluated with the NFaiRR metric, which measures bias in a ranked list based on an aggregation over the unbiasedness scor…
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…
Generating Synthetic Documents for Cross-Encoder Re-Rankers: A Comparative Study of ChatGPT and Human Experts
Arian Askari, Mohammad Aliannejadi, Evangelos Kanoulas +1
We investigate the usefulness of generative Large Language Models (LLMs) in generating training data for cross-encoder re-rankers in a novel direction: generating synthetic documen…
Injecting the BM25 Score as Text Improves BERT-Based Re-rankers
Arian Askari, Amin Abolghasemi, Gabriella Pasi +2
In this paper we propose a novel approach for combining first-stage lexical retrieval models and Transformer-based re-rankers: we inject the relevance score of the lexical model as…