Summary of Hindillm: Large Language Model For Hindi, by Sanjay Chouhan et al.
HindiLLM: Large Language Model for Hindi
by Sanjay Chouhan, Shubha Brata Nath, Aparajita Dutta
First submitted to arxiv on: 29 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The advancements in Large Language Models (LLMs) have led to significant progress in solving various language processing problems, with a focus on English due to its popularity online. However, there is a lack of high-performance LLMs for Hindi and other Indic languages. This paper addresses this gap by pre-training two autoregressive LLM models for Hindi, namely HindiLLM-Small and HindiLLM-Medium. The approach involves a two-step process: unsupervised pre-training using a large text corpus and supervised fine-tuning for specific tasks like sentiment analysis, text classification, natural language inference, and multiple-choice question-answering. The evaluation shows that the HindiLLM-based fine-tuned models outperform existing models in most language-related tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about building better computer models to understand and generate Indian languages, especially Hindi. This has important implications for communication and technology in India. The researchers created two special computer models called HindiLLM-Small and HindiLLM-Medium that can learn from large amounts of text data without human help. They then fine-tuned these models for specific tasks like understanding sentiment, classifying texts, and answering questions. The results show that their approach works well and is better than other methods. |
Keywords
» Artificial intelligence » Autoregressive » Fine tuning » Inference » Question answering » Supervised » Text classification » Unsupervised
