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101 Billion Arabic Words Dataset (2405.01590v1)

Published 29 Apr 2024 in cs.CL

Abstract: In recent years, LLMs have revolutionized the field of natural language processing, showcasing an impressive rise predominantly in English-centric domains. These advancements have set a global benchmark, inspiring significant efforts toward developing Arabic LLMs capable of understanding and generating the Arabic language with remarkable accuracy. Despite these advancements, a critical challenge persists: the potential bias in Arabic LLMs, primarily attributed to their reliance on datasets comprising English data that has been translated into Arabic. This reliance not only compromises the authenticity of the generated content but also reflects a broader issue -the scarcity of original quality Arabic linguistic data. This study aims to address the data scarcity in the Arab world and to encourage the development of Arabic LLMs that are true to both the linguistic and nuances of the region. We undertook a large-scale data mining project, extracting a substantial volume of text from the Common Crawl WET files, specifically targeting Arabic content. The extracted data underwent a rigorous cleaning and deduplication process, using innovative techniques to ensure the integrity and uniqueness of the dataset. The result is the 101 Billion Arabic Words Dataset, the largest Arabic dataset available to date, which can significantly contribute to the development of authentic Arabic LLMs. This study not only highlights the potential for creating linguistically and culturally accurate Arabic LLMs but also sets a precedent for future research in enhancing the authenticity of Arabic LLMs.

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References (29)
  1. Harika Abburi et al. Generative ai text classification using ensemble llm approaches. arXiv preprint arXiv:2309.07755, 2023.
  2. Arabicaqa: A comprehensive dataset for arabic question answering. arXiv preprint arXiv:2403.17848, 2024.
  3. Ahmed Abdelali et al. Larabench: Benchmarking arabic ai with large language models. 2024.
  4. Aramus: Pushing the limits of data and model scale for arabic natural language processing. arXiv preprint arXiv:2306.06800, 2023.
  5. ARBML: Democritizing Arabic natural language processing tools. In Eunjeong L. Park, Masato Hagiwara, Dmitrijs Milajevs, Nelson F. Liu, Geeticka Chauhan, and Liling Tan (eds.), Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS), pp.  8–13, Online, November 2020. Association for Computational Linguistics. 10.18653/v1/2020.nlposs-1.2. URL https://aclanthology.org/2020.nlposs-1.2.
  6. Cidar: Culturally relevant instruction dataset for arabic, 2024.
  7. Alaaeldin El-Nouby et al. Are large-scale datasets necessary for self-supervised pre-training? 2021.
  8. William Held et al. A material lens on coloniality in nlp. arXiv preprint arXiv:2311.08391, 2023.
  9. Hanlei Jin et al. A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods. arXiv preprint arXiv:2403.02901, 2024.
  10. Unsung challenges of building and deploying language technologies for low resource language communities. pp.  211–219, 2019. URL https://aclanthology.org/2019.icon-1.25.
  11. Vincent Jung and Lonneke van der Plas. Understanding the effects of language-specific class imbalance in multilingual fine-tuning. 2024.
  12. Thomas Lancaster. A large language model supported synthesis of contemporary academic integrity research trends. 2024.
  13. Zhenyu Li et al. Flexkbqa: A flexible llm-powered framework for few-shot knowledge base question answering. 38(17), 2024.
  14. Low-resource languages: A review of past work and future challenges. arXiv preprint arXiv:2006.07264, 2020.
  15. Frontiers in linguistic annotation for lower-density languages. 2006.
  16. Masayasu Muraoka et al. Cross-lingual transfer of large language model by visually-derived supervision toward low-resource languages. 2023.
  17. CAMeL tools: An open source python toolkit for Arabic natural language processing. In Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, and Stelios Piperidis (eds.), Proceedings of the Twelfth Language Resources and Evaluation Conference, pp.  7022–7032, Marseille, France, May 2020. European Language Resources Association. ISBN 979-10-95546-34-4. URL https://aclanthology.org/2020.lrec-1.868.
  18. Arquad: An expert-annotated arabic machine reading comprehension dataset. Cognitive Computation, pp.  1–20, 2024.
  19. The refinedweb dataset for falcon llm: outperforming curated corpora with web data, and web data only. arXiv preprint arXiv:2306.01116, 2023.
  20. Data cards: Purposeful and transparent dataset documentation for responsible ai. 2022. 10.1145/3531146.3533231. URL https://doi.org/10.1145/3531146.3533231.
  21. Nathaniel R. Robinson et al. Chatgpt mt: Competitive for high-(but not low-) resource languages. arXiv preprint arXiv:2309.07423, 2023.
  22. Neha Sengupta and et al. Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. 2023.
  23. Jais and jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models. arXiv preprint arXiv:2308.16149, 2023.
  24. Shivalika Singh and et al. Aya dataset: An open-access collection for multilingual instruction tuning. 2024.
  25. Low resource arabic dialects transformer neural machine translation improvement through incremental transfer of shared linguistic features. Arabian Journal for Science and Engineering, pp.  1–17, 2024.
  26. Karthik Valmeekam et al. Large language models still can’t plan (a benchmark for llms on planning and reasoning about change). arXiv preprint arXiv:2206.10498, 2022.
  27. Xinyi Wang et al. Large language models are implicitly topic models: Explaining and finding good demonstrations for in-context learning. 2023.
  28. The kind dataset: A social collaboration approach for nuanced dialect data collection. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pp.  32–43, 2024.
  29. Yifan Yao et al. A survey on large language model (llm) security and privacy: The good, the bad, and the ugly. pp.  100211, 2024.
Citations (1)
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Summary

  • The paper demonstrates the creation of a vast Arabic dataset containing 101 billion words to bridge the language resource gap.
  • It details a rigorous data acquisition and cleaning process that includes URL filtering, deduplication, normalization, and dediacritization.
  • The dataset paves the way for developing advanced Arabic NLP models, enhancing linguistic representation and inclusivity in AI.

Exploring the 101 Billion Arabic Words Dataset for NLP

The Significance of Arabic in NLP

While the proliferation of LLMs has significantly advanced the field of NLP (Natural Language Processing), much of this progress has centered around the English language. Arabic, spoken by over 400 million people and rich in cultural history, has not seen equivalent advancements, partly due to the scarcity and inadequacy of focused datasets. This gap in resources has made the development of robust Arabic-centric models slow, affecting the representation and processing of the language in digital contexts.

The Introduction of a Vast Arabic Dataset

Responding to these challenges, researchers have developed the 101 Billion Arabic Words Dataset, a sizeable consolidated corpus of Arabic text intended to catalyze the development of NLP models that can proficiently handle the nuances of the language. With over 101 billion words of pure Arabic content, this dataset aims to bridge the gap in language resources and allow for the creation of models that are not only performant but culturally and linguistically authentic.

Methodology: Building a Reliable Dataset

Data Acquisition and Initial Processing

The dataset was assembled from the Common Crawl archive, filtering and processing web pages to extract Arabic content. Over a period spanning several months, extensive data extraction was conducted, sifting through 0.8 petabytes of data which underscores the enormity and comprehensiveness of this endeavor.

Cleaning and Preprocessing

Before a dataset can be used to train models, it must be cleansed and preprocessed to remove noise and ensure consistency. Here's how the researchers ensured the quality of the Arabic dataset:

  • URL Filtering and Deduplication: Initial steps involved filtering out undesirable URLs and removing duplicates to ensure that the dataset contained unique and relevant content only.
  • Textual Cleaning Procedures: This included removing HTML tags, correcting encoding issues, dealing with special characters, and eliminating both overly brief and lengthy text segments.
  • Normalization and Dediacritization: To standardize the text and simplify computational requirements, the dataset underwent processes to normalize characters and remove diacritical marks.

Advanced Text Cleaning Techniques

Utilizing Yamane’s formula, a subset of documents was examined in-depth for issues like inappropriate content or formatting errors. Advanced tools and programming environments such as Python, Rust, and AWS technologies were leveraged to efficiently handle and process the vast amounts of data.

Challenges and Limitations

The scale of this dataset and the complexity of Arabic script presented unique challenges:

  • Ensuring Quality at Scale: Manual inspection methods had to be limited due to the dataset's size, posing challenges in fully guaranteeing text quality.
  • Ethical Concerns: Filtering out inappropriate and biased content was critical yet challenging, and the researchers acknowledged the limitations of URL-based filtering techniques in capturing sensitive content comprehensively.

Implications and Future Directions

The creation and refinement of the 101 Billion Arabic Words Dataset mark a pivotal step towards resolving the disparity in language resources available for Arabic. It provides a robust foundation for developing advanced Arabic LLMs that respect linguistic and cultural nuances.

Furthermore, this extensive dataset not only has the potential to drive advancements in Arabic NLP but also sets a precedent for similar endeavors in other underrepresented languages. As more data becomes available and computational resources grow, we can expect to see the rise of more linguistically diverse and culturally aware AI systems.

Conclusion

The development of the 101 Billion Arabic Words Dataset is a substantial move towards equitable technological advancement in NLP. While it opens up numerous possibilities for research and application in the Arabic linguistic domain, it also highlights the ongoing need for resources that cater to a broader linguistic landscape, promising a more inclusive digital future.

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