Summary of Efficient Continual Pre-training Of Llms For Low-resource Languages, by Arijit Nag et al.
Efficient Continual Pre-training of LLMs for Low-resource Languages
by Arijit Nag, Soumen Chakrabarti, Animesh Mukherjee, Niloy Ganguly
First submitted to arxiv on: 13 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 proposed method aims to improve the performance of open-source large language models (OsLLMs) on low-resource languages (LRLs) by reducing the cost of continual pre-training (CPT). This is achieved through a novel algorithm for selecting a subset of texts from a larger corpus, which requires very little CPT data. Additionally, the authors develop an algorithm to select tokens to include in the LLM vocabulary, leading to further performance improvements. The study uses the recent Llama-3 model and nine Indian languages with diverse scripts and resource availability to evaluate the effectiveness of these techniques. The evaluation is conducted using IndicGenBench, a generation task benchmark dataset for Indic languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to help open-source large language models work better on languages that don’t have much training data. This means fewer words and less information to learn from. To make this happen, the authors created two new ways to improve the model: choosing which texts to use and adding more vocabulary words. They tested these ideas using a specific type of Indian language and found that they worked well. |
Keywords
» Artificial intelligence » Llama
