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Supervisor: Dr. Samabia Tehsin

 

Generative AI and Large Language Models for Enhancing Access to Multilingual Digital Governance in Pakistan

 

Supervisor: Dr. Samabia Tehsin, Professor

Campus/School/Dept: BUIC

RAC Approved Supervisor for Research Areas: Explainable AI, Deep Learning, Machine Learning, NLP, Generative AI

Supervisory Record:

  • PhD Produced: 01
  • PhD Enrolled: 02
  • MS/MPhil Produced: 17
  • MS/MPhil Enrolled:01

 

Topic Brief Description:

The rapid advancements in Generative AI and Large Language Models (LLMs) present opportunities to address societal challenges in linguistically diverse and resource-constrained regions like Pakistan. This research will explore the design, adaptation, and deployment of generative AI systems tailored for low-resource languages and underrepresented communities. The study aims to enhance digital inclusivity, accessibility, and empowerment by developing tools and methodologies to bridge the gap between technology and linguistically diverse populations.

 

Research Objectives/Deliverables:

  1. Develop methodologies to adapt generative AI models to low-resource languages while maintaining accuracy and ethical considerations.
  2. Create tools or frameworks that ensure scalability across diverse linguistic and socio-economic backgrounds.
  3. Propose and evaluate applications in areas like education, governance, and social inclusion to demonstrate impact.
  4. Investigate biases and risks associated with deploying LLMs in culturally sensitive contexts, ensuring responsible AI usage.

 

Research Questions: 

  1. How can generative AI models be adapted for low-resource languages with minimal labeled data?
  2. What techniques can be used to ensure fairness, inclusivity, and cultural sensitivity in generative AI systems?
  3. How can LLMs be optimized for multilingual and multi-dialect environments prevalent in resource-constrained regions?
  4. What are the societal, ethical, and technical challenges in deploying generative AI systems in linguistically diverse contexts?
  5. How can AI-driven tools enhance access to education, governance, or healthcare in low-resource settings?

 

Candidate’s Eligibility Profile:

  1. The applicant should possess a solid foundation in mathematics, logical reasoning, and problem-solving skills.
  2. Proficiency in programming languages such as Python and familiarity with frameworks like TensorFlow or PyTorch is highly recommended.
  3. Prior exposure to research methodologies, literature reviews, and scientific writing.
  4. Preferably experience or coursework in deep learning, NLP, and generative AI.
  5. Demonstrated ability to solve technical challenges and manage complex projects.