PAC: Analyzing Efficacy of Pivot Techniques in LLMs for Low-Resource Languages
Status: ArchivedAuthors: Uzair Ahmed, Muhammad Faizan Zahid, Huzama Ahmad, James Thorne, Alice Oh
This study investigates the effectiveness of Large Language Models (LLMs) in processing Low Resource Languages (LRLs) using a novel approach called Pivot-Assisted Consensus (PAC), which integrates a pivot language with a multi-source consensus mechanism. A comprehensive series of ablation experiments were conducted to evaluate the performance of this method on various linguistic tasks, including mathematical reasoning, sentiment analysis, and natural language inference. The research shows that linguistic and cultural compatibility play a crucial role in pivot language selection, leading to a significant improvement in task accuracy across all examined scenarios. It highlights the significance of cultural awareness and the utilization of multiple language resources to overcome data scarcity for LRLs, resulting in more sophisticated and accurate LLM outputs. In addition, we provide comprehensive analyses of our results to enhance the comprehension of LLM capabilities, which facilitates the development of more transparent and interpretable models.
- multilingual
- cultural