# How desirable is alignment between LLMs and linguistically diverse human users?

arXiv: https://arxiv.org/abs/2502.12884 · HTML: https://mlsanity.com/paper/2502.12884/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 81 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [Large Language Model Alignment: A Survey](https://mlsanity.com/paper/2309.15025/) (2023)
- [Lifting the Curse of Multilinguality by Pre-training Modular Transformers](https://mlsanity.com/paper/2205.06266/) (2022)
- [Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback](https://mlsanity.com/paper/2303.05453/) (2023)
- [Understanding User Experience in Large Language Model Interactions](https://mlsanity.com/paper/2401.08329/) (2024)
- [Investigating Gender Bias in Turkish Language Models](https://mlsanity.com/paper/2404.11726/) (2024)

## Abstract (arXiv)

We discuss how desirable it is that Large Language Models (LLMs) be able to adapt or align their language behavior with users who may be diverse in their language use. User diversity may come about among others due to i) age differences; ii) gender characteristics, and/or iii) multilingual experience, and associated differences in language processing and use. We consider potential consequences for usability, communication, and LLM development.