RakutenAI-7B: Extending Large Language Models for Japanese

Core Concepts

RakutenAI-7B introduces advanced Japanese-oriented large language models, achieving top performance in language understanding benchmarks.

Abstract

1. Introduction: Shift towards "Pre-Train, Prompt, and Predict" paradigm in NLP. Focus on large language models (LLMs) for various NLP tasks. 2. Technical Details: 2.1 Tokenization: Mistral tokenizer challenges with encoding single Japanese characters into multiple tokens. Extending the tokenizer to improve character-per-token rate for Japanese text processing. 2.2 Foundation Model Training: Importance of high-quality data for pre-training LLMs. Data filtering techniques to enhance dataset quality for training. 2.3 Model Fine-tuning: Fine-tuning foundation model to create specialized instruction and chat-tuned models. Safety measures implemented to prevent generation of harmful content. 2.4 Evaluation: 2.4.1 Evaluation Settings: Performance evaluation using Japanese and English versions of LM-Harness across various NLP tasks. 2.4.2 Evaluation Results for Foundation Models: RakutenAI-7B outperforms other Japanese models on both Japanese and English test sets. 2.4.3 Evaluation Results for Instruction-Tuned Models: Improvement in performance of instruction-tuned models over foundation model observed. 3. Conclusion: RakutenAI-7B demonstrates high performance across diverse NLP tasks in both Japanese and English languages. 4. Acknowledgements & Limitations mentioned.

Stats

By improving the tokenization process for Japanese, we can achieve cost efficient text processing during model training as well as inference. We train the released models on approximately 175 billion tokens of filtered data. Our instruct model achieves an average score of 68.74, leading by almost 2 points over Youri-7B-instruction, the second best model. Our model achieves an average score of 60.50, while Japanese-StableLM-Base-Gamma-7b lags by more than 4 points compared to RakutenAI-7B. Our instruct model achieves an average score of 61.32, leading by almost 5 points over Youri-7B-instruction, the second best model.

Quotes

"We release our models to the public under the Apache 2.0 License." "Our aim is to help the community create more affordable and efficient Japanese language models."

Key Insights Distilled From

RakutenAI-7B

by Raku... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15484.pdf

Deeper Inquiries

How can RakutenAI's approach be applied to other languages beyond English and Japanese

RakutenAI's approach can be applied to other languages beyond English and Japanese by following a systematic initiative similar to RakutenAI-7B. This involves leveraging the latest technologies in natural language processing (NLP) to develop large language models tailored for specific languages. The key steps include: Tokenization Optimization: Extend the tokenizer vocabulary with additional tokens specifically selected for the target language, improving character-per-token rates. Foundation Model Training: Curate high-quality datasets for pre-training, filtering out personally identifiable information, and ensuring data quality through normalization and deduplication techniques. Model Fine-tuning: Fine-tune the foundation model using a mix of open and internally crafted datasets, focusing on specific tasks or domains relevant to the target language. Evaluation: Evaluate model performance across a diverse range of NLP tasks using standardized benchmarks to ensure robustness and effectiveness. By replicating this approach for different languages, organizations can create large language models that excel in understanding and generating text in those languages, opening up opportunities for more efficient communication tools across various linguistic contexts.

What potential biases or limitations might arise from using large language models like RakutenAI

Using large language models like RakutenAI may introduce potential biases or limitations due to several factors: Data Biases: Models trained on biased datasets may perpetuate stereotypes or prejudices present in the training data when generating text. Ethical Concerns: Inappropriate content generation such as misinformation, offensive responses, or harmful biases could arise if not monitored carefully during fine-tuning stages. Linguistic Limitations: Large language models might struggle with certain linguistic nuances or dialects within a given language, leading to inaccuracies in text generation. Performance Disparities: Variability in model performance across different tasks or domains could result in uneven outcomes based on task complexity or dataset characteristics. To mitigate these issues, continuous monitoring of model behavior is essential along with ethical guidelines governing their use to ensure responsible deployment of large language models.

How can advancements in large language models impact real-world applications beyond natural language processing

Advancements in large language models have far-reaching implications beyond natural language processing (NLP), impacting real-world applications such as: Automated Translation Services: Enhanced LLMs can improve translation accuracy between multiple languages rapidly without human intervention. Content Creation: Content generation platforms powered by advanced LLMs can automate writing processes for articles, reports, marketing materials with minimal human input. Virtual Assistants: More sophisticated conversational agents enabled by LLMs offer personalized assistance across industries like customer service and healthcare support systems. 4Medical Diagnosis: Advanced LLMs aid medical professionals by analyzing patient records efficiently for diagnosis recommendations based on symptoms provided. These advancements revolutionize how businesses operate by streamlining processes that rely heavily on textual data analysis while enhancing user experiences through more intelligent interactions facilitated by AI-driven solutions built upon cutting-edge LLM technology."