Why do LLMs have emergent properties?
a year ago
- #emergence
- #machine-learning
- #large-language-models
- Large language models (LLMs) exhibit emergence behaviors when scaled to certain parameter counts, suddenly enabling new tasks.
- Emergence is common in nature (e.g., phase changes) and machine learning (e.g., regression error drops abruptly with increased parameters).
- In algorithms, capabilities can emerge abruptly when a critical threshold (e.g., gate count) is reached, enabling new functionalities.
- LLMs allocate parameter bits across many tasks; when enough bits are allocated to a specific task, its capability 'suddenly' appears.
- Predicting the emergence of new capabilities in LLMs is challenging, especially for complex, undefined tasks like creating resonant stories.
- Emergence in LLMs is plausible due to high-dimensional optimization and vast parameter spaces, making new behaviors probable over time.