VI-MISTRAL-X: Building a Vietnamese Language Model with Advanced Continual Pre-Training

Core Concepts

vi-mistral-x is an innovative Large Language Model designed for the Vietnamese language, utilizing continual pre-training to enhance understanding and generation capabilities.

Abstract

Abstract:

Introduction of vi-mistral-x, a Large Language Model for Vietnamese.
Utilizes continual pre-training based on Mistral architecture.
Outperforms existing models in text classification, question answering, and text generation.
Proposed Method:
Effective Corpus Preparation:

Random selection reduces corpus size efficiently.
N-gram-based filtering ensures dataset uniqueness.
BERT-based classifier filters out toxic content.
Perplexity-based filtering enhances document quality.
Effective Tokenizer Training:

Google SentencePiece used to train new SPM model.
Rule-based token filtering focuses on Vietnamese character recognition.
Hybrid tokenizer integrates original Mistral's SPM model with enhanced SPM.
Effective Model Initialization:

Adaptation of Mistral architecture for Vietnamese token embeddings.
Expansion of embedding layer and language model head for Vietnamese-specific tokens.
Effective Model Training:

Focus on memory and computational efficiency in training LLMs.
Optimization through curated model architectures and parallelism techniques.
Model Alignment:

Fine-tuning vi-mistral-x on task-specific Vietnamese datasets for optimal performance.
Experimental Results:
Pretrained Model Comparison:

Evaluation of vi-mistral-x against other models in various tasks like CLM and VMLU.
Finetuned Model (Updating):
Detailed evaluation results of vi-mistral-x across different categories in VMLU benchmark suite.

Stats

vi-mistral-xは、CLM（次のトークン予測）タスクで、トークン数2068480、損失2.1566、精度0.5622を達成しました。

Quotes

Key Insights Distilled From

Vi-Mistral-X

by James Vo at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15470.pdf

Deeper Inquiries

言語モデルの開発において、他の言語にも同様のアプローチが適用可能か？

この研究で使用された手法やアプローチは、他の言語にも適用可能です。例えば、Vietnamese language modelで採用されたcontinual pre-trainingやtokenizer trainingなどの技術は、他の言語モデルでも応用することができます。各言語固有の特性やニュアンスを考慮したカスタマイズが必要ですが、基本的なフレームワークや方法論は多くの言語に適用可能です。