Summary of Bongllama: Llama For Bangla Language, by Abdullah Khan Zehady et al.
BongLLaMA: LLaMA for Bangla Language
by Abdullah Khan Zehady, Safi Al Mamun, Naymul Islam, Santu Karmaker
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces BongLLaMA, an open-source large language model fine-tuned exclusively on Bangla corpora and instruction-tuning datasets. Despite being the 5th largest spoken language in the world, Bangla is still a “low-resource” language, making existing models struggle to perform well on Bangla Language Processing (BLP) tasks. The authors present their methodology, data augmentation techniques, fine-tuning details, and comprehensive benchmarking results showcasing BongLLaMA’s utility on BLP tasks. With the aim of facilitating future benchmarking studies focused on this widely-spoken yet “low-resource” language, the authors believe BongLLaMA will serve as the new standard baseline for Bangla Language Models. The models are available for public use at https://huggingface.co/BanglaLLM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new tool called BongLLaMA that helps computers understand the Bengali language better. Bengali is spoken by many people, but it’s hard to find good models that can understand and generate Bengali text. The researchers used big datasets of Bengali texts to train their model, which they call BongLLaMA. They tested it on different tasks and showed how well it performed compared to other models. This new tool will help scientists and developers create better tools for Bengali language processing. |
Keywords
» Artificial intelligence » Data augmentation » Fine tuning » Instruction tuning » Large language model
