# Quantifying Language Disparities in Multilingual Large Language Models

arXiv: https://arxiv.org/abs/2508.17162 · HTML: https://mlsanity.com/paper/2508.17162/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 23 Aug 2025.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning](https://mlsanity.com/paper/2005.00333/) (2020)
- [MEGA: Multilingual Evaluation of Generative AI](https://mlsanity.com/paper/2303.12528/) (2023)
- [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://mlsanity.com/paper/1908.11828/) (2019)
- [XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation](https://mlsanity.com/paper/2104.07412/) (2021)
- [Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages](https://mlsanity.com/paper/2212.05409/) (2022)
- [MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation](https://mlsanity.com/paper/2503.10497/) (2025)
- [Evaluating Evaluation Metrics: A Framework for Analyzing NLG Evaluation Metrics using Measurement Theory](https://mlsanity.com/paper/2305.14889/) (2023)
- [M5 -- A Diverse Benchmark to Assess the Performance of Large Multimodal Models Across Multilingual and Multicultural Vision-Language Tasks](https://mlsanity.com/paper/2407.03791/) (2024)
- [Beyond English-Centric Bitexts for Better Multilingual Language Representation Learning](https://mlsanity.com/paper/2210.14867/) (2022)
- [A Systematic Study of Performance Disparities in Multilingual Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2310.12892/) (2023)

## Built on by (most cited first, 7 of 7)

- [Knowledge Localization in Mixture-of-Experts LLMs Using Cross-Lingual Inconsistency](https://mlsanity.com/paper/2603.17102/) (2026)
- [UPDESH: Synthesizing Grounded Instruction Tuning Data for 13 Indic Languages](https://mlsanity.com/paper/2509.21294/) (2025)
- [Donors and Recipients: On Asymmetric Transfer Across Tasks and Languages with Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2511.13368/) (2025)
- [Misalignment of LLM-Generated Personas with Human Perceptions in Low-Resource Settings](https://mlsanity.com/paper/2512.02058/) (2025)
- [Multilingual in Name Only? Cultural and Linguistic Weaknesses of LLMs in Urdu](https://mlsanity.com/paper/2609.10758/) (2026)
- [Encoded but Not Decoded: Layer-Localized Evidence for a Three-Level Gap in LLM Syntax](https://mlsanity.com/paper/2609.29848/) (2026)
- [Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs](https://mlsanity.com/paper/2610.14442/) (2026)

## Abstract (arXiv)

Results reported in large-scale multilingual evaluations are often fragmented and confounded by factors such as target languages, differences in experimental setups, and model choices. We propose a framework that disentangles these confounding variables and introduces three interpretable metrics--the performance realisation ratio, its coefficient of variation, and language potential--enabling a finer-grained and more insightful quantification of actual performance disparities across both (i) models and (ii) languages. Through a case study of 13 model variants on 11 multilingual datasets, we demonstrate that our framework provides a more reliable measurement of model performance and language disparities, particularly for low-resource languages, which have so far proven challenging to evaluate. Importantly, our results reveal that higher overall model performance does not necessarily imply greater fairness across languages.