How Network Theory Might Explain Emergent Abilities in AI

Key Points:

• There is debate about whether abilities like analogical reasoning emerging in AI models are real or an artifact of how we evaluate them.
• Interacting with powerful language models leads many to intuit that new understanding is emerging beyond what was input.
• A new study applies network theory to explore how complex skills can emerge in language models.
• The network concept maps relationships between "skill nodes" and "text nodes," tracking AI competence.
• As models grow, new connections form between skills and texts, enabling handling of more complex language.
• The study suggests huge potential for models to develop skills without direct exposure, going beyond "stochastic parrots."

Do Large Language Models such as ChatGPT exhibit emergent properties? For instance, when asked to compare a concept from one domain to an unrelated domain, ChatGPT comes up with a creative analogy, which feels like an emergent understanding of abstract relationships. Or math: some LLMs have demonstrated an emergent problem-solving ability which was not directly taught but developed from the models' exposure to mathematical language and logic in their training datasets.

There has been much debate about this phenomenon. One of the top papers at NeurIPS 2023 (entitled Are Emergent Abilities of Large Language Models a Mirage?) suggested that what we perceive as the spontaneous development of new capabilities is actually an artifact of the metrics researchers use to evaluate these systems. The seemingly sudden leaps in AI performance and adaptability could be more about how we measure success than about any intrinsic evolution of the AI itself.

But if you’ve spent a lot of time with a powerful language model, I’d wager your intuitions are that there is something definitively going on here. You might have been providing AI with just a handful of examples or a brief context to guide its responses then something unexpected happens. As you interact more, ChatGPT starts to respond with insights and ideas that show an understanding of concepts well beyond your expectations. You get a glimpse into the phenomenon of emergence in AI, where the system exhibits abilities that seem to transcend its inputs, hinting at a deeper, almost intuitive grasp of the task at hand.

If you’re as fascinated as we are by emergence and complexity in general, you’ll want to know what researchers from Princeton and DeepMind found when they applied network theory to this problem.