What are the Differences Between Open Source LLMs and Closed Source LLMs?
Transparency and Customizability:
- Open Source: Enables inspection, evaluation, reproduction, and customization. Researchers can modify the architecture and apply transfer learning using pre-trained models.
- Closed Source: Offers limited customization due to proprietary technical details, keeping algorithms secret and resulting in a lack of transparency.
Computational Requirements:
- Open Source: These models often leverage donated computing resources, which can limit large-scale development to some extent.
- Closed Source: These models generally are backed by big tech companies with substantial financial resources, enabling large-scale development (e.g., OpenAI spent millions training GPT-3 on cloud infrastructure).
To learn more about the differences between open source LLMs and closed source LLMs, please read How to Choose Between Open Source and Closed Source LLMs: A 2024 Guide
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