Benchmarking the Performance of Pre-trained LLMs across Urdu NLP Tasks

TL;DR

This paper benchmarks 7 large multilingual language models on 17 Urdu NLP tasks, revealing that models with richer language data often outperform larger models with less Urdu coverage, and SOTA models excel mainly in encoder-decoder architectures.

Contribution

It provides a comprehensive evaluation of prominent LLMs on Urdu NLP tasks, highlighting the importance of language-specific data over model size and comparing performance against SOTA models.

Findings

SOTA models outperform encoder-decoder models in most Urdu NLP tasks.

Llama 3.1-8B surpasses Llama 2-7B-Chat with better language coverage.

Smaller models with richer Urdu data often outperform larger models with less Urdu diversity.

Abstract

Large Language Models (LLMs) pre-trained on multilingual data have revolutionized natural language processing research, by transitioning from languages and task specific model pipelines to a single model adapted on a variety of tasks. However majority of existing multilingual NLP benchmarks for LLMs provide evaluation data in only few languages with little linguistic diversity. In addition these benchmarks lack quality assessment against the respective state-of the art models. This study presents an in-depth examination of 7 prominent LLMs: GPT-3.5-turbo, Llama 2-7B-Chat, Llama 3.1-8B, Bloomz 3B, Bloomz 7B1, Ministral-8B and Whisper (Large, medium and small variant) across 17 tasks using 22 datasets, 13.8 hours of speech, in a zero-shot setting, and their performance against state-of-the-art (SOTA) models, has been compared and analyzed. Our experiments show that SOTA models currently outperform encoder-decoder models in majority of Urdu NLP tasks under zero-shot settings. However, comparing Llama 3.1-8B over prior version Llama 2-7B-Chat, we can deduce that with improved language coverage, LLMs can surpass these SOTA models. Our results emphasize that models with fewer parameters but richer language-specific data, like Llama 3.1-8B, often outperform larger models with lower language diversity, such as GPT-3.5, in several tasks.