Summary of Pipeline Analysis For Developing Instruct Llms in Low-resource Languages: a Case Study on Basque, by Ander Corral and Ixak Sarasua and Xabier Saralegi
Pipeline Analysis for Developing Instruct LLMs in Low-Resource Languages: A Case Study on Basque
by Ander Corral, Ixak Sarasua, Xabier Saralegi
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 presents an analysis of strategies to develop a large language model (LLM) capable of following instructions in the low-resource language Basque. The approach focuses on three stages: pre-training, instruction tuning, and alignment with human preferences. The findings demonstrate that continual pre-training with a high-quality Basque corpus improves natural language understanding by over 12 points. Instruction tuning and human preference alignment using automatically translated datasets prove highly effective, resulting in a 24-point improvement in instruction-following performance. The resulting models establish a new state-of-the-art for Basque in the sub-10B parameter category. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about developing a special kind of computer program that can understand and follow instructions in a language called Basque. Basque is not as well-known or widely used as some other languages, which makes it harder to create programs that work with it. The researchers tried different approaches to improve the program’s ability to understand and follow instructions in Basque. They found that by using a big dataset of Basque words and phrases, they could make the program much better at understanding language. They also used automatically translated datasets to help the program learn what makes sense in Basque. As a result, the program became really good at following instructions in Basque. |
Keywords
» Artificial intelligence » Alignment » Instruction tuning » Language understanding » Large language model
