AI Briefing2023 Open LLM Trends and Technical Summary
Key point
This summarizes the development process of open source LLMs in 2023 and their core technical components.
Details
2023 was a year when interest in open source LLMs surged and the debate with closed models intensified. Open models play an important role in research reproducibility, community engagement, verification of model bias, and reducing carbon emissions.
Core Components of LLMs
- Architecture: Most high-performance LLMs today use a decoder-only Transformer structure.
- Dataset & Tokenizer: Text is converted into token units for training, with data scale ranging from hundreds of billions to trillions of tokens.
- Weights & Fine-tuning: When the trained parameters, called weights, are made public, users can perform fine-tuning optimized for specific tasks based on them, allowing cost-efficient use of the model.
Shift in Technical Paradigm Until 2022, the main trend was Scaling Laws and emergent abilities, which focused on increasing model parameter size, but 2023 marked a shift toward emphasizing the importance of data over model size.
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