Research: Evaluating Parameter-Efficient Fine-Tuning Methods for Large Language Models ...

Research question: How effective are parameter-efficient fine-tuning techniques such as LoRA and QLoRA in adapting large language models for low-resource languages compared to conventional fine-tuning methods?

Background & Motivation: Large Language Models (LLMs) have demonstrated remarkable performance across a variety of tasks, but their adaptation to low-resource languages remains challenging due to limited data and computational constraints. Recent advances in parameter-efficient fine-tuning methods, such as LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA), promise cost-effective and scalable solutions for adapting LLMs.

Research Gap: Despite these advancements, there is limited empirical evidence on how these methods perform specifically for low-resource languages, where scarcity of data and unique linguistic characteristics may pose additional challenges. The effectiveness of LoRA and QLoRA in such contexts, compared to traditional fine-tuning, is underexplored.

Approach & Expected Contribution: This study will systematically review the literature, formulate hypotheses, and design experiments using publicly available low-resource language datasets. The project will compare LoRA and QLoRA against standard fine-tuning approaches in terms of adaptation quality, computational efficiency, and linguistic coverage, providing rigorous evaluation metrics and analysis.

Why It Matters: Findings from this research will inform best practices for scalable and accessible NLP solutions in underserved languages, supporting inclusivity and broader applicability of AI technology. The study contributes empirical evidence for the development of efficient, equitable foundation models.