Summary of Leveraging Parameter Efficient Training Methods For Low Resource Text Classification: a Case Study in Marathi, by Pranita Deshmukh et al.
Leveraging Parameter Efficient Training Methods for Low Resource Text Classification: A Case Study in Marathi
by Pranita Deshmukh, Nikita Kulkarni, Sanhita Kulkarni, Kareena Manghani, Raviraj Joshi
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 proposed research investigates the application of Parameter Efficient Fine-Tuning (PEFT) methods to develop advanced Natural Language Processing (NLP) techniques for the low-resource language Marathi. Specifically, the study focuses on fine-tuning Large Language Models (LLMs), such as BERT, to reduce computational costs while achieving comparable results to fully fine-tuned models. The authors analyze various PEFT methods applied to monolingual and multilingual Marathi BERT models, evaluating their performance on prominent text classification datasets like MahaSent, MahaHate, and MahaNews. The results show that incorporating PEFT techniques significantly accelerates model training speed, addressing a critical aspect of model development and deployment. Additionally, the study explores Low-Rank Adaptation of Large Language Models (LoRA) and adapter methods for low-resource text classification, demonstrating their competitiveness with full fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at ways to make computers understand Marathi language better. It’s like a big puzzle where we need to find the right pieces to fit together. The team used a method called Parameter Efficient Fine-Tuning (PEFT) to help computers learn Marathi faster and more efficiently. They tested this method on different types of Marathi text and found that it worked really well! This is important because it can help us make computers understand Marathi language better, which can be helpful for things like translating books or helping people communicate. |
Keywords
» Artificial intelligence » Bert » Fine tuning » Lora » Low rank adaptation » Natural language processing » Nlp » Parameter efficient » Text classification
