Summary of Domain-adaptative Continual Learning For Low-resource Tasks: Evaluation on Nepali, by Sharad Duwal et al.
Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali
by Sharad Duwal, Suraj Prasai, Suresh Manandhar
First submitted to arxiv on: 18 Dec 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 paper explores domain-adaptive pre-training (DAPT) for continual learning, specifically adapting large language models (LLMs) to new domains with limited data availability. The authors evaluate DAPT’s feasibility in a low-resource setting using synthetic data to train Llama 3 8B on the Nepali language. They assess the adapted model’s performance, forgetting, and knowledge acquisition, comparing it to the base model on Nepali generation tasks and popular benchmarks. The results show some forgetting but also surprising retention, with increasing evaluation shots leading to better percent increases in the final model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how machines can learn from new information without needing a lot of data. It’s like trying to teach someone a new language – it’s hard to do it all at once! So, researchers came up with an idea called domain-adaptive pre-training (DAPT). They wanted to see if this could work for languages that don’t have much data available. In this case, they chose the Nepali language and used computers to “train” a model on some fake information. Then, they tested how well it did at speaking and understanding Nepali, comparing it to the original model. The results were pretty cool – even though there was some forgetting, the model still learned a lot! |
Keywords
» Artificial intelligence » Continual learning » Llama » Synthetic data
