Too Late to Train, Too Early To Use? A Study on Necessity and Viability of Low-Resource Bengali LLMs

TL;DR

This study evaluates the necessity and viability of dedicated Bengali LLMs, comparing existing models' performance on various tasks and highlighting challenges like tokenization inefficiencies and dataset biases.

Contribution

It provides a comprehensive comparison of open-weight and closed-source LLMs on Bengali tasks and discusses the current limitations hindering the development of effective Bengali-specific LLMs.

Findings

LLMs perform variably on Bengali script generation tasks

Tokenization inefficiencies increase computational costs

Biases in machine-translated datasets affect model performance

Abstract

Each new generation of English-oriented Large Language Models (LLMs) exhibits enhanced cross-lingual transfer capabilities and significantly outperforms older LLMs on low-resource languages. This prompts the question: Is there a need for LLMs dedicated to a particular low-resource language? We aim to explore this question for Bengali, a low-to-moderate resource Indo-Aryan language native to the Bengal region of South Asia. We compare the performance of open-weight and closed-source LLMs such as LLaMA-3 and GPT-4 against fine-tuned encoder-decoder models across a diverse set of Bengali downstream tasks, including translation, summarization, paraphrasing, question-answering, and natural language inference. Our findings reveal that while LLMs generally excel in reasoning tasks, their performance in tasks requiring Bengali script generation is inconsistent. Key challenges include inefficient tokenization of Bengali script by existing LLMs, leading to increased computational costs and potential performance degradation. Additionally, we highlight biases in machine-translated datasets commonly used for Bengali NLP tasks. We conclude that there is a significant need for a Bengali-oriented LLM, but the field currently lacks the high-quality pretraining and instruction-tuning datasets necessary to develop a highly effective model.