Thanks for the post. I think it'd be helpful if you could add some links to references for some of the things you say, such as:
For instance, between 10^10 and 10^11 parameters, models showed dramatic improvements in their ability to interpret emoji sequences representing movies.
Recent research into large language models (LLMs) has revealed fascinating patterns in how these systems develop capabilities. While initial discussions of "emergent abilities" suggested sudden, discontinuous jumps in performance, closer analysis reveals a more nuanced picture that warrants careful examination.
The Data Behind Emergence
The concept of emergence in LLMs was first systematically studied through the BIG-bench benchmark. Initial observations suggested that capabilities like emoji movie interpretation appeared to emerge suddenly at certain model scales. For instance, between 10^10 and 10^11 parameters, models showed dramatic improvements in their ability to interpret emoji sequences representing movies. [1]
However, these apparent discontinuities deserve closer scrutiny. When we examine the actual data:
Understanding Multi-Step Reasoning
One compelling explanation for apparently emergent behavior comes from examining multi-step reasoning. Consider a task requiring ten consecutive correct reasoning steps. Even if a model's ability to perform individual reasoning steps improves smoothly, the probability of completing the entire chain successfully can show a sharp, seemingly discontinuous jump.
This matches what we observe in practice. Tasks requiring multiple steps of reasoning or complex chains of thought tend to show more apparent "emergence" than simpler tasks, even though the underlying capabilities may be improving gradually.
Scaling Laws and Practical Limitations
Recent research from Google DeepMind (the Chinchilla paper) has shown that optimal training requires about 20 tokens of training data for each parameter in the model. This creates practical limits on scaling:
These constraints help explain why we haven't seen models scaled to the size that early GPT-4 rumors suggested (100T parameters). The limiting factor isn't just compute - it's the availability of quality training data.
Implications for AI Development
This more nuanced understanding of emergence has important implications:
Moving Forward
Rather than focusing on emergence as a mysterious phenomenon, we should:
The development of LLM capabilities is more predictable than initial observations suggested, but this makes the field no less fascinating. Understanding these patterns helps us better predict and guide the development of these powerful systems.
https://www.quantamagazine.org/the-unpredictable-abilities-emerging-from-large-ai-models-20230316/