Refairmulate: A Benchmark Dataset for Gender-Fair Query Reformulations

Hai Son Le, Shirin Seyedsalehi, Morteza Zihayat, Ebrahim Bagheri
January 2026

Abstract

This paper introduces Refairmulate, a benchmark dataset for gender-fair query reformulations, enabling research on developing fair and unbiased query rewriting techniques in information retrieval systems.

Type
Conference paper
Publication
European Conference on Information Retrieval

This work presents Refairmulate, a comprehensive benchmark dataset for evaluating and developing gender-fair query reformulation methods.

Query Reformulation Gender Fairness Benchmark Dataset Information Retrieval Fairness
Hai Son Le
Hai Son Le
Master’s Student

Hai Son Le is a Master’s student in the Human-Centered Machine Intelligence Lab, working on research projects in machine learning and data science.

Shirin Seyedsalehi
PhD Student (Alumni)

Shirin Seyedsalehi is a former PhD student and alumna of the Human-Centered Machine Intelligence Lab.

Morteza Zihayat
Morteza Zihayat
Principal Investigator

Dr. Morteza Zihayat is a Canada Research Chair (CRC) in Human-Centered AI and Associate Professor at Toronto Metropolitan University, Faculty of Engineering and Architectural Science. He also holds appointments as Adjunct Associate Professor at the University of Waterloo (Management Sciences) and IBM Faculty Fellow at IBM Centre for Advanced Studies. He is the Director of the Human-Centered Machine Intelligence Lab.