# Performance in a dialectal profiling task of LLMs for varieties of Brazilian Portuguese

arXiv: https://arxiv.org/abs/2410.10991 · HTML: https://mlsanity.com/paper/2410.10991/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination](https://mlsanity.com/paper/2406.08818/) (2024)
- [DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules](https://mlsanity.com/paper/2305.13406/) (2023)
- [The Sociolinguistic Foundations of Language Modeling](https://mlsanity.com/paper/2407.09241/) (2024)
- [Sabi'a-2: A New Generation of Portuguese Large Language Models](https://mlsanity.com/paper/2403.09887/) (2024)
- [What Truly Matters? Using Linguistic Cues for Analyzing the #BlackLivesMatter Movement and its Counter Protests: 2013 to 2020](https://mlsanity.com/paper/2109.12192/) (2021)
- [Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of African-American English](https://mlsanity.com/paper/2206.08978/) (2022)

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

- [P3B3: A Multi-Turn Conversational Benchmark for Measuring European and Brazilian Portuguese Variety Bias in LLMs](https://mlsanity.com/paper/2606.16753/) (2026)
- [Diversidade lingu'istica e inclus~ao digital: desafios para uma ia brasileira](https://mlsanity.com/paper/2411.01259/) (2024)
- [Can I guess where you are from? Modeling dialectal morphosyntactic similarities in Brazilian Portuguese](https://mlsanity.com/paper/2603.20695/) (2026)

## Abstract (arXiv)

Different of biases are reproduced in LLM-generated responses, including dialectal biases. A study based on prompt engineering was carried out to uncover how LLMs discriminate varieties of Brazilian Portuguese, specifically if sociolinguistic rules are taken into account in four LLMs: GPT 3.5, GPT-4o, Gemini, and Sabi.-2. The results offer sociolinguistic contributions for an equity fluent NLP technology.