# Language Bias in Information Retrieval: The Nature of the Beast and Mitigation Methods

arXiv: https://arxiv.org/abs/2509.06195 · HTML: https://mlsanity.com/paper/2509.06195/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 36 references that are in the map.
- First posted on arXiv 7 Sep 2025.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [Equity of Attention: Amortizing Individual Fairness in Rankings](https://mlsanity.com/paper/1805.01788/) (2018)
- [Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval](https://mlsanity.com/paper/2108.08787/) (2021)
- [mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset](https://mlsanity.com/paper/2108.13897/) (2021)
- [FA*IR: A Fair Top-k Ranking Algorithm](https://mlsanity.com/paper/1706.06368/) (2017)
- [MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer](https://mlsanity.com/paper/2109.00904/) (2021)
- [Morphology Matters: A Multilingual Language Modeling Analysis](https://mlsanity.com/paper/2012.06262/) (2020)
- [Quantifying the Impact of User Attention on Fair Group Representation in Ranked Lists](https://mlsanity.com/paper/1901.10437/) (2019)
- [Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey](https://mlsanity.com/paper/2002.06612/) (2020)
- [Neural Approaches to Multilingual Information Retrieval](https://mlsanity.com/paper/2209.01335/) (2022)
- [Soft Prompt Decoding for Multilingual Dense Retrieval](https://mlsanity.com/paper/2305.09025/) (2023)
- [Multi-EuP: The Multilingual European Parliament Dataset for Analysis of Bias in Information Retrieval](https://mlsanity.com/paper/2311.01870/) (2023)
- [Language Fairness in Multilingual Information Retrieval](https://mlsanity.com/paper/2405.00978/) (2024)

## Built on by (most cited first, 3 of 3)

- [CLEAR: Cross-Lingual Enhancement in Alignment via Reverse-training](https://mlsanity.com/paper/2604.05821/) (2026)
- [LANGSAE EDITING: Improving Multilingual Information Retrieval via Post-hoc Language Identity Removal](https://mlsanity.com/paper/2601.04768/) (2026)
- [Retrieval Disagreement as a Signal: Diagnosing and Steering Multilingual Dense Embedders](https://mlsanity.com/paper/2610.18526/) (2026)

## Abstract (arXiv)

Language fairness in multilingual information retrieval (MLIR) systems is crucial for ensuring equitable access to information across diverse languages. This paper sheds light on the issue, based on the assumption that queries in different languages, but with identical semantics, should yield equivalent ranking lists when retrieving on the same multilingual documents. We evaluate the degree of fairness using both traditional retrieval methods, and a DPR neural ranker based on mBERT and XLM-R. Additionally, we introduce `LaKDA', a novel loss designed to mitigate language biases in neural MLIR approaches. Our analysis exposes intrinsic language biases in current MLIR technologies, with notable disparities across the retrieval methods, and the effectiveness of LaKDA in enhancing language fairness.