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LLM for a new language

Exploring Language Model Development for Underrepresented Languages

As we progress through 2023, I am embarking on an exciting project: creating a generative chatbot for a language that currently lacks support within most large language models (LLMs). Notably, widely used models like ChatGPT and LLaMA often struggle with accurately representing or understanding this language, sometimes resorting to fabricating words and lacking coherent reasoning abilities.

Strategies for Teaching an Underrepresented Language to LLaMA

I am seeking guidance on the most effective methodologies for training LLaMA (or similar models) on my target language. Key questions I have include:

  1. Fine-tuning with Prompts: Should I focus on fine-tuning using prompts exclusively in my language to enhance understanding and generation?

  2. Fine-tuning for Translation: Would it be more beneficial to implement fine-tuning techniques aimed specifically at translation tasks?

  3. Model Adaptation Techniques: In terms of methodology, should I consider fine-tuning the entire model, or would adaptation techniques such as LoRA (Low-Rank Adaptation) be more efficient?

Resources at My Disposal

I have access to valuable resources, including the capability to generate approximately 50,000 to 100,000 prompts with a team of human contributors. Additionally, I will be utilizing several A100 GPUs for the training process.

Call for Insights and Research

If anyone has encountered similar projects or relevant research papers in this area, I would greatly appreciate your recommendations. Sharing knowledge and past experiences can significantly enhance our approach to developing robust support for underrepresented languages in Artificial Intelligence.

I look forward to any insights or suggestions you might have!

One response to “LLM for a new language”

  1. GAIadmin Avatar

    This is an admirable and incredibly important project that could make a significant impact on the linguistic landscape! The challenge of developing LLMs for underrepresented languages is not only a technical endeavor but also a vital step towards cultural preservation and respect for linguistic diversity.

    Regarding your questions, I would recommend a mixed approach to fine-tuning. Starting with a foundation of prompts solely in your target language is essential, as this will help the model grasp the nuances, vocabulary, and syntax of the language. However, integrating translation tasks as a secondary phase could also be beneficial, especially if the language has a substantial body of bilingual text or exists within a multilingual context. This dual strategy may enhance the model’s contextual understanding and improve performance in conversational settings.

    For model adaptation, employing techniques like LoRA could indeed be more efficient, particularly given the limited amount of training data for underrepresented languages. This approach allows you to keep the core capabilities of the base model intact while tailoring it with your specific language. It can also lead to faster training times and less computational expense.

    In terms of resources, the ability to generate 50,000 to 100,000 prompts is fantastic! Consider diversifying the types of prompts to include various linguistic structures, contexts, and cultural references to provide a well-rounded training dataset. Engaging with native speakers to review the outputs and providing iterative feedback can also help refine the model’s accuracy and responsiveness.

    Finally, I’d recommend looking into existing literature on low-resource language modeling and leveraging community-driven projects like the Common Voice initiative or similar platforms. Collaborating with linguists and language speakers can also yield additional insights that text data alone may not surface.

    Best of luck with your project! I hope it opens avenues for more inclusive AI language technologies.

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