# How does a Multilingual LM Handle Multiple Languages?

arXiv: https://arxiv.org/abs/2502.04269 · HTML: https://mlsanity.com/paper/2502.04269/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 8 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [What you can cram into a single vector: Probing sentence embeddings for linguistic properties](https://mlsanity.com/paper/1805.01070/) (2018)
- [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019)
- [XLM-E: Cross-lingual Language Model Pre-training via ELECTRA](https://mlsanity.com/paper/2106.16138/) (2021)
- [Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOM](https://mlsanity.com/paper/2303.01911/) (2023)
- [Analyzing the Evaluation of Cross-Lingual Knowledge Transfer in Multilingual Language Models](https://mlsanity.com/paper/2402.02099/) (2024)
- [Measuring Sentences Similarity: A Survey](https://mlsanity.com/paper/1910.03940/) (2019)
- [Cross-lingual transfer of multilingual models on low resource African Languages](https://mlsanity.com/paper/2409.10965/) (2024)

## Built on by (most cited first, 1 of 1)

- [Code-Mixed Telugu-English Hate Speech Detection](https://mlsanity.com/paper/2502.10632/) (2025)

## Abstract (arXiv)

Multilingual language models have significantly advanced due to rapid progress in natural language processing. Models like BLOOM 1.7B, trained on diverse multilingual datasets, aim to bridge linguistic gaps. However, their effectiveness in capturing linguistic knowledge, particularly for low-resource languages, remains an open question. This study critically examines MLMs capabilities in multilingual understanding, semantic representation, and cross-lingual knowledge transfer. While these models perform well for high-resource languages, they struggle with less-represented ones. Additionally, traditional evaluation methods often overlook their internal syntactic and semantic encoding. This research addresses key limitations through three objectives. First, it assesses semantic similarity by analyzing multilingual word embeddings for consistency using cosine similarity. Second, it examines BLOOM-1.7B and Qwen2 through Named Entity Recognition and sentence similarity tasks to understand their linguistic structures. Third, it explores cross-lingual knowledge transfer by evaluating generalization from high-resource to low-resource languages in sentiment analysis and text classification. By leveraging linguistic probing, performance metrics, and visualizations, this study provides insights into the strengths and limitations of MLMs. The findings aim to enhance multilingual NLP models, ensuring better support for both high- and low-resource languages, thereby promoting inclusivity in language technologies.