Thamar Solorio from the University of Houston will discuss machine learning approaches for spontaneous human language processing. The talk will cover adapting multilingual transformers to code-switching data and using data augmentation for domain adaptation in sequence labeling tasks. Solorio will also provide an overview of other research projects at the RiTUAL lab, focusing on the scarcity of labeled data. Why it matters: This presentation addresses key challenges in Arabic NLP related to data scarcity, which is a persistent obstacle in developing effective AI applications for the region.
Researchers fine-tuned the Qwen2-1.5B model for Arabic using QLoRA on a 4GB VRAM system, using datasets like Bactrian and Arabic Wikipedia. They addressed challenges in Arabic NLP including morphology and dialectal variations. After 10,000 training steps, the final loss converged to 0.1083 with improved handling of Arabic-specific linguistic phenomena. Why it matters: This demonstrates a resource-efficient approach for creating specialized Arabic language models, democratizing access to advanced NLP technologies.
MBZUAI Assistant Professor Alham Fikri Aji is presenting research at EACL 2024 on efficient NLP for low-resource languages. The study uses knowledge distillation, transferring knowledge from a larger model (ChatGPT) to a smaller one using synthetic instruction data. The goal is to achieve similar performance with less computational resources, focusing on underrepresented languages. Why it matters: This work addresses the need for more accessible and inclusive NLP technologies, especially for languages lacking extensive datasets and computational resources.
Dr. Teresa Lynn from Dublin City University (DCU) discussed the challenges in developing NLP tools for Irish, a low-resource language facing digital extinction. She highlighted the lack of speech and language applications and fundamental language resources for Irish. Lynn also mentioned her work at DCU on the GaelTech project and her involvement in the European Language Equality project. Why it matters: The development of NLP tools for low-resource languages like Irish is crucial for preserving linguistic diversity and preventing digital marginalization in the AI era.
MBZUAI President Eric Xing led a global collaboration to develop Vicuna, an LLM alternative to GPT-3 addressing the unsustainable costs of training LLMs. OpenAI CEO Sam Altman acknowledged Abu Dhabi's role in the global AI conversation, building off of achievements like Vicuna. Xing and colleagues are publishing research at MLSys 2023 on "cross-mesh resharding" to improve computer communication in deep learning, aiming for low-carbon, affordable, and miniaturized AI. Why it matters: This research signals a push towards sustainable AI development in the region, emphasizing efficiency and reduced environmental impact.
MBZUAI's Hanan Al Darmaki is working to improve automated speech recognition (ASR) for low-resource languages, where labeled data is scarce. She notes that Arabic presents unique challenges due to dialectal variations and a lack of written resources corresponding to spoken dialects. Al Darmaki's research focuses on unsupervised speech recognition to address this gap. Why it matters: Overcoming these challenges can improve virtual assistant effectiveness across diverse languages and enable more inclusive AI applications in the Arabic-speaking world.
MBZUAI Ph.D. graduate Hilal Mohammad Hilal AlQuabeh researched methods to improve the efficiency of machine learning algorithms, specifically focusing on pairwise learning and multi-instance learning. Pairwise learning teaches AI to make decisions by comparing options in pairs, useful for ranking and anomaly detection. Multi-instance learning involves learning from sets of data points, applicable in areas like drug discovery. Why it matters: Optimizing AI for low-resource environments expands its accessibility and applicability in critical sectors like healthcare and remote area operations.
Researchers from MBZUAI, University of British Columbia, and Monash University have created LaMini-LM, a collection of small language models distilled from ChatGPT. LaMini-LM is trained on a dataset of 2.58M instructions and can be deployed on consumer laptops and mobile devices. The smaller models perform almost as well as larger counterparts while addressing security concerns. Why it matters: This work enables the deployment of LLMs in resource-constrained environments and enhances data security by reducing reliance on cloud-based LLMs.