Summary of Juru: Legal Brazilian Large Language Model From Reputable Sources, by Roseval Malaquias Junior et al.
Juru: Legal Brazilian Large Language Model from Reputable Sources
by Roseval Malaquias Junior, Ramon Pires, Roseli Romero, Rodrigo Nogueira
First submitted to arxiv on: 26 Mar 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 paper presents an investigation into two strategies for reducing the computational costs associated with pretraining large language models. The first strategy is domain specialization, where the model is trained on a specific domain’s data, and the second strategy is pretraining with high-quality data. The authors explore these strategies by specializing the Sabiá-2 Small model using 1.9 billion unique tokens from Brazilian legal sources and evaluating its performance on legal and general knowledge exams. They propose a model called Juru that demonstrates the benefits of domain specialization, but notes that this comes at the expense of degrading performance in other knowledge areas within the same language. The study contributes to the growing body of evidence showing that pretraining data selection can enhance the performance of large language models, enabling their exploration at a lower cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to make big language models cheaper and more efficient to train. Two main ideas are explored: using special kinds of data (like legal documents) and using high-quality data. The authors test these ideas by training a model on Brazilian legal texts and seeing how well it does on tests. They create a new model called Juru that works better when focused on one type of knowledge, but might not be as good at other things. This study shows that choosing the right data can make big language models more useful and affordable. |
Keywords
* Artificial intelligence * Pretraining
