How Well Do LLMs Understand Tunisian Arabic?
- URL: http://arxiv.org/abs/2511.16683v1
- Date: Wed, 12 Nov 2025 15:23:41 GMT
- Title: How Well Do LLMs Understand Tunisian Arabic?
- Abstract summary: Large Language Models (LLMs) are the engines driving today's AI agents.<n>Yet, the ability of industrial-scale LLMs to comprehend low-resource languages, such as Tunisian Arabic, is often overlooked.<n>This neglect risks excluding millions of Tunisians from fully interacting with AI in their own language, pushing them toward French or English.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Large Language Models (LLMs) are the engines driving today's AI agents. The better these models understand human languages, the more natural and user-friendly the interaction with AI becomes, from everyday devices like computers and smartwatches to any tool that can act intelligently. Yet, the ability of industrial-scale LLMs to comprehend low-resource languages, such as Tunisian Arabic (Tunizi), is often overlooked. This neglect risks excluding millions of Tunisians from fully interacting with AI in their own language, pushing them toward French or English. Such a shift not only threatens the preservation of the Tunisian dialect but may also create challenges for literacy and influence younger generations to favor foreign languages. In this study, we introduce a novel dataset containing parallel Tunizi, standard Tunisian Arabic, and English translations, along with sentiment labels. We benchmark several popular LLMs on three tasks: transliteration, translation, and sentiment analysis. Our results reveal significant differences between models, highlighting both their strengths and limitations in understanding and processing Tunisian dialects. By quantifying these gaps, this work underscores the importance of including low-resource languages in the next generation of AI systems, ensuring technology remains accessible, inclusive, and culturally grounded.
Related papers
- The Landscape of Arabic Large Language Models (ALLMs): A New Era for Arabic Language Technology [12.32863495152587]
The Arabic world faces distinct challenges in developing Arabic-specific Large Language Models.<n>This article explores the trajectory of ALLMs, from their inception to the present day.<n>We also discuss the challenges and opportunities that ALLMs present for the Arab world.
arXiv Detail & Related papers (2025-06-02T05:45:19Z) - Generative AI, Pragmatics, and Authenticity in Second Language Learning [0.0]
There are obvious benefits to integrating generative AI (artificial intelligence) into language learning and teaching.
However, due to how AI systems under-stand human language, they lack the lived experience to be able to use language with the same social awareness as humans.
There are built-in linguistic and cultural biases based on their training data which is mostly in English and predominantly from Western sources.
arXiv Detail & Related papers (2024-10-18T11:58:03Z) - SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages [77.75535024869224]
We present SeaLLMs 3, the latest iteration of the SeaLLMs model family, tailored for Southeast Asian languages.
SeaLLMs 3 aims to bridge this gap by covering a comprehensive range of languages spoken in this region, including English, Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao, Tamil, and Javanese.
Our model excels in tasks such as world knowledge, mathematical reasoning, translation, and instruction following, achieving state-of-the-art performance among similarly sized models.
arXiv Detail & Related papers (2024-07-29T03:26:22Z) - Distributed agency in second language learning and teaching through generative AI [0.0]
ChatGPT can provide informal second language practice through chats in written or voice forms.
Instructors can use AI to build learning and assessment materials in a variety of media.
arXiv Detail & Related papers (2024-03-29T14:55:40Z) - How do Large Language Models Handle Multilingualism? [81.15060972112563]
This study explores how large language models (LLMs) handle multilingualism.
LLMs initially understand the query, converting multilingual inputs into English for task-solving.
In the intermediate layers, they employ English for thinking and incorporate multilingual knowledge with self-attention and feed-forward structures.
arXiv Detail & Related papers (2024-02-29T02:55:26Z) - Arabic Sentiment Analysis with Noisy Deep Explainable Model [48.22321420680046]
This paper proposes an explainable sentiment classification framework for the Arabic language.
The proposed framework can explain specific predictions by training a local surrogate explainable model.
We carried out experiments on public benchmark Arabic SA datasets.
arXiv Detail & Related papers (2023-09-24T19:26:53Z) - AceGPT, Localizing Large Language Models in Arabic [73.39989503874634]
The paper proposes a comprehensive solution that includes pre-training with Arabic texts, Supervised Fine-Tuning (SFT) utilizing native Arabic instructions, and GPT-4 responses in Arabic.
The goal is to cultivate culturally cognizant and value-aligned Arabic LLMs capable of accommodating the diverse, application-specific needs of Arabic-speaking communities.
arXiv Detail & Related papers (2023-09-21T13:20:13Z) - Neural Machine Translation for the Indigenous Languages of the Americas:
An Introduction [102.13536517783837]
Most languages from the Americas are among them, having a limited amount of parallel and monolingual data, if any.
We discuss the recent advances and findings and open questions, product of an increased interest of the NLP community in these languages.
arXiv Detail & Related papers (2023-06-11T23:27:47Z) - Understanding Natural Language Understanding Systems. A Critical
Analysis [91.81211519327161]
The development of machines that guillemotlefttalk like usguillemotright, also known as Natural Language Understanding (NLU) systems, is the Holy Grail of Artificial Intelligence (AI)
But never has the trust that we can build guillemotlefttalking machinesguillemotright been stronger than the one engendered by the last generation of NLU systems.
Are we at the dawn of a new era, in which the Grail is finally closer to us?
arXiv Detail & Related papers (2023-03-01T08:32:55Z) - Do As I Can, Not As I Say: Grounding Language in Robotic Affordances [119.29555551279155]
Large language models can encode a wealth of semantic knowledge about the world.
Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language.
We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions.
arXiv Detail & Related papers (2022-04-04T17:57:11Z) - TEET! Tunisian Dataset for Toxic Speech Detection [0.0]
The Tunisian dialect is a combination of many other languages like MSA, Tamazight, Italian and French.
Because of its rich language, dealing with NLP problems can be challenging due to the lack of large annotated datasets.
This paper introduces a new annotated dataset composed of approximately 10k of comments.
arXiv Detail & Related papers (2021-10-11T14:00:08Z) - A Panoramic Survey of Natural Language Processing in the Arab World [12.064637486695485]
The term natural language refers to any system of symbolic communication (spoken, signed or written) without intentional human planning and design.
Natural language processing (NLP) is the sub-field of artificial intelligence (AI) focused on modeling natural languages to build applications such as speech recognition and synthesis, machine translation, optical character recognition (OCR), sentiment analysis (SA), question answering, dialogue systems, etc.
arXiv Detail & Related papers (2020-11-25T10:45:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.