Mubeen AI: A Specialized Arabic Language Model for Heritage Preservation and User Intent Understanding
- URL: http://arxiv.org/abs/2510.23271v1
- Date: Mon, 27 Oct 2025 12:29:27 GMT
- Title: Mubeen AI: A Specialized Arabic Language Model for Heritage Preservation and User Intent Understanding
- Abstract summary: Mubeen is a proprietary Arabic language model developed by MASARAT SA.<n>It is optimized for deep understanding of Arabic linguistics, Islamic studies, and cultural heritage.<n>The model incorporates seminal scholarly works in linguistics, jurisprudence, hadith, and Quranic exegesis.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Mubeen is a proprietary Arabic language model developed by MASARAT SA, optimized for deep understanding of Arabic linguistics, Islamic studies, and cultural heritage. Trained on an extensive collection of authentic Arabic sources significantly expanded by digitizing historical manuscripts via a proprietary Arabic OCR engine, the model incorporates seminal scholarly works in linguistics, jurisprudence, hadith, and Quranic exegesis, alongside thousands of academic theses and peer-reviewed research papers. Conditioned through a deep linguistic engineering framework, Mubeen masters not just the meaning but the eloquence of Arabic, enabling precise understanding across classical texts, contemporary writing, and regional dialects with focus on comprehending user intent and delivering accurate, contextually relevant responses. Unlike other Arabic models relying on translated English data that often fail in intent detection or retrieval-augmented generation (RAG), Mubeen uses native Arabic sources to ensure cultural authenticity and accuracy. Its core innovation is the Practical Closure Architecture, designed to solve the "Utility Gap Crisis" where factually correct answers fail to resolve users' core needs, forcing them into frustrating cycles of re-prompting. By prioritizing clarity and decisive guidance, Mubeen transforms from an information repository into a decisive guide, aligning with Saudi Vision 2030. The model's architecture combines deep heritage specialization with multi-disciplinary expert modules, enabling robust performance across both cultural preservation and general knowledge domains.
Related papers
- Grounding Arabic LLMs in the Doha Historical Dictionary: Retrieval-Augmented Understanding of Quran and Hadith [0.0]
We develop a retrieval-augmented generation framework grounded in diachronic lexicographic knowledge.<n>Unlike prior RAG systems that rely on general-purpose corpora, our approach retrieves evidence from the Doha Historical Dictionary of Arabic.
arXiv Detail & Related papers (2026-03-25T06:09:42Z) - DialectalArabicMMLU: Benchmarking Dialectal Capabilities in Arabic and Multilingual Language Models [54.10223256792762]
We present DialectalArabicMMLU, a new benchmark for evaluating the performance of large language models (LLMs) across Arabic dialects.<n>We extend the MMLU-Redux framework through manual translation and adaptation of 3K multiple-choice question-answer pairs into five major dialects.
arXiv Detail & Related papers (2025-10-31T15:17:06Z) - Liaozhai through the Looking-Glass: On Paratextual Explicitation of Culture-Bound Terms in Machine Translation [70.43884512651668]
We formalize Genette's (1987) theory of paratexts from literary and translation studies to introduce the task of paratextual explicitation for machine translation.<n>We construct a dataset of 560 expert-aligned paratexts from four English translations of the classical Chinese short story collection Liaozhai.<n>Our findings demonstrate the potential of paratextual explicitation in advancing machine translation beyond linguistic equivalence.
arXiv Detail & Related papers (2025-09-27T16:27:36Z) - MuDRiC: Multi-Dialect Reasoning for Arabic Commonsense Validation [30.670712065855902]
We introduce MuDRiC, an extended Arabic commonsense dataset incorporating multiple dialects, and (ii) a novel method adapting Graph Convolutional Networks (GCNs) to Arabic commonsense reasoning.<n>Our work enhances Arabic natural language understanding by providing both a foundational dataset and a novel method for handling its complex variations.
arXiv Detail & Related papers (2025-08-18T17:42:53Z) - Enhanced Arabic Text Retrieval with Attentive Relevance Scoring [12.053940320312355]
Arabic poses a particular challenge for natural language processing and information retrieval.<n>Despite the growing global significance of Arabic, it is still underrepresented in NLP research and benchmark resources.<n>We present an enhanced Dense Passage Retrieval framework developed specifically for Arabic.
arXiv Detail & Related papers (2025-07-31T10:18:28Z) - Arabizi vs LLMs: Can the Genie Understand the Language of Aladdin? [0.4751886527142778]
Arabizi is a hybrid form of Arabic that incorporates Latin characters and numbers.<n>It poses significant challenges for machine translation due to its lack of formal structure.<n>This research project investigates the model's performance in translating Arabizi into both Modern Standard Arabic and English.
arXiv Detail & Related papers (2025-02-28T11:37:52Z) - Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion [70.23624194206171]
This paper addresses the need for democratizing large language models (LLM) in the Arab world.<n>One practical objective for an Arabic LLM is to utilize an Arabic-specific vocabulary for the tokenizer that could speed up decoding.<n>Inspired by the vocabulary learning during Second Language (Arabic) Acquisition for humans, the released AraLLaMA employs progressive vocabulary expansion.
arXiv Detail & Related papers (2024-12-16T19:29:06Z) - 101 Billion Arabic Words Dataset [0.0]
This study aims to address the data scarcity in the Arab world and to encourage the development of Arabic Language Models.
We undertook a large-scale data mining project, extracting a substantial volume of text from the Common Crawl WET files.
The extracted data underwent a rigorous cleaning and deduplication process, using innovative techniques to ensure the integrity and uniqueness of the dataset.
arXiv Detail & Related papers (2024-04-29T13:15:03Z) - ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic [51.922112625469836]
We present datasetname, the first multi-task language understanding benchmark for the Arabic language.
Our data comprises 40 tasks and 14,575 multiple-choice questions in Modern Standard Arabic (MSA) and is carefully constructed by collaborating with native speakers in the region.
Our evaluations of 35 models reveal substantial room for improvement, particularly among the best open-source models.
arXiv Detail & Related papers (2024-02-20T09:07:41Z) - Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking [48.21982147529661]
This paper introduces a novel approach for massively multicultural knowledge acquisition.
Our method strategically navigates from densely informative Wikipedia documents on cultural topics to an extensive network of linked pages.
Our work marks an important step towards deeper understanding and bridging the gaps of cultural disparities in AI.
arXiv Detail & Related papers (2024-02-14T18:16:54Z) - 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)
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.