Summary of Airavata: Introducing Hindi Instruction-tuned Llm, by Jay Gala et al.
Airavata: Introducing Hindi Instruction-tuned LLM
by Jay Gala, Thanmay Jayakumar, Jaavid Aktar Husain, Aswanth Kumar M, Mohammed Safi Ur Rahman Khan, Diptesh Kanojia, Ratish Puduppully, Mitesh M. Khapra, Raj Dabre, Rudra Murthy, Anoop Kunchukuttan
First submitted to arxiv on: 26 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 Airavata is a language model designed for Hindi, specifically tuned for assistive tasks. The model was created by fine-tuning OpenHathi with diverse datasets to improve its performance on instruction-based tasks. Alongside the model, researchers also share the IndicInstruct dataset, which provides a collection of datasets for further research into Indic language models. Additionally, the paper presents evaluation benchmarks and a framework for assessing LLM performance across various tasks in Hindi. The goal is to expand Airavata’s capabilities to all 22 scheduled Indic languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Airavata is a special kind of computer program that can understand and generate text in Hindi. It was made better by fine-tuning it with lots of data from different sources. This helps the model do tasks like giving instructions or answering questions more accurately. Researchers also created a big collection of datasets called IndicInstruct to help others improve their own language models for Indian languages. The goal is to make Airavata work with all 22 scheduled Indian languages. |
Keywords
* Artificial intelligence * Fine tuning * Language model
