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310 results on '"Alnumay A"'

1. ALLaM: Large Language Models for Arabic and English

Author

Bari, M Saiful, Alnumay, Yazeed, Alzahrani, Norah A., Alotaibi, Nouf M., Alyahya, Hisham A., AlRashed, Sultan, Mirza, Faisal A., Alsubaie, Shaykhah Z., Alahmed, Hassan A., Alabduljabbar, Ghadah, Alkhathran, Raghad, Almushayqih, Yousef, Alnajim, Raneem, Alsubaihi, Salman, Mansour, Maryam Al, Alrubaian, Majed, Alammari, Ali, Alawami, Zaki, Al-Thubaity, Abdulmohsen, Abdelali, Ahmed, Kuriakose, Jeril, Abujabal, Abdalghani, Al-Twairesh, Nora, Alowisheq, Areeb, and Khan, Haidar

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

We present ALLaM: Arabic Large Language Model, a series of large language models to support the ecosystem of Arabic Language Technologies (ALT). ALLaM is carefully trained considering the values of language alignment and knowledge transfer at scale. Our autoregressive decoder-only architecture models demonstrate how second-language acquisition via vocabulary expansion and pretraining on a mixture of Arabic and English text can steer a model towards a new language (Arabic) without any catastrophic forgetting in the original language (English). Furthermore, we highlight the effectiveness of using parallel/translated data to aid the process of knowledge alignment between languages. Finally, we show that extensive alignment with human preferences can significantly enhance the performance of a language model compared to models of a larger scale with lower quality alignment. ALLaM achieves state-of-the-art performance in various Arabic benchmarks, including MMLU Arabic, ACVA, and Arabic Exams. Our aligned models improve both in Arabic and English from their base aligned models.

Published
2024

2. When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Author

Alzahrani, Norah, Alyahya, Hisham Abdullah, Alnumay, Yazeed, Alrashed, Sultan, Alsubaie, Shaykhah, Almushaykeh, Yusef, Mirza, Faisal, Alotaibi, Nouf, Altwairesh, Nora, Alowisheq, Areeb, Bari, M Saiful, and Khan, Haidar

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Large Language Model (LLM) leaderboards based on benchmark rankings are regularly used to guide practitioners in model selection. Often, the published leaderboard rankings are taken at face value - we show this is a (potentially costly) mistake. Under existing leaderboards, the relative performance of LLMs is highly sensitive to (often minute) details. We show that for popular multiple-choice question benchmarks (e.g., MMLU), minor perturbations to the benchmark, such as changing the order of choices or the method of answer selection, result in changes in rankings up to 8 positions. We explain this phenomenon by conducting systematic experiments over three broad categories of benchmark perturbations and identifying the sources of this behavior. Our analysis results in several best-practice recommendations, including the advantage of a hybrid scoring method for answer selection. Our study highlights the dangers of relying on simple benchmark evaluations and charts the path for more robust evaluation schemes on the existing benchmarks. The code for this paper is available at https://github.com/National-Center-for-AI-Saudi-Arabia/lm-evaluation-harness., Comment: updated with ACL 2024 camera ready version

Published
2024

4. Learning to Read Analog Gauges from Synthetic Data

Author

Leon-Alcazar, Juan, Alnumay, Yazeed, Zheng, Cheng, Trigui, Hassane, Patel, Sahejad, and Ghanem, Bernard

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Manually reading and logging gauge data is time inefficient, and the effort increases according to the number of gauges available. We present a computer vision pipeline that automates the reading of analog gauges. We propose a two-stage CNN pipeline that identifies the key structural components of an analog gauge and outputs an angular reading. To facilitate the training of our approach, a synthetic dataset is generated thus obtaining a set of realistic analog gauges with their corresponding annotation. To validate our proposal, an additional real-world dataset was collected with 4.813 manually curated images. When compared against state-of-the-art methodologies, our method shows a significant improvement of 4.55 in the average error, which is a 52% relative improvement. The resources for this project will be made available at: https://github.com/fuankarion/automatic-gauge-reading.

Published
2023

17. ALLaM: Large Language Models for Arabic and English.

Author

M. Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani, Nouf M. Alotaibi, Hisham Abdullah Alyahya, Sultan Alrashed, Faisal Mirza, Shaykhah Alsubaie, Hassan A. Alahmed, Ghadah Alabduljabbar, Raghad Alkhathran, Yousef Almushayqih, Raneem Alnajim, Salman Alsubaihi, Maryam Al Mansour, Majed Alrubaian, Ali Alammari, Zaki Alawami, Abdulmohsen Al-Thubaity, Ahmed Abdelali, Jeril Kuriakose, Abdalghani Abujabal, Nora Al-Twairesh, Areeb Alowisheq, and Haidar Khan

Published
2024
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