Summary of Emergence Of Hidden Capabilities: Exploring Learning Dynamics in Concept Space, by Core Francisco Park et al.
Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space
by Core Francisco Park, Maya Okawa, Andrew Lee, Hidenori Tanaka, Ekdeep Singh Lubana
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, called the concept space, analyzes a model’s learning dynamics by representing each axis as an independent concept underlying the data generating process. This approach identifies how the speed at which a concept is learned and the order of concept learning are controlled by properties of the data termed concept signal. The analysis reveals moments of sudden turns in the direction of a model’s learning dynamics, corresponding to the emergence of hidden capabilities. These latent interventions show that the model possesses the capability to manipulate a concept, but these capabilities cannot be elicited via naive input prompting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative models are getting better and better at doing things like creating new images or text. But scientists still don’t fully understand how they work. This paper tries to figure out what makes generative models learn and how they develop new abilities during training. The researchers propose a new way of looking at how models learn by using something called the concept space. They show that when a model learns certain concepts, it can suddenly develop new abilities that it didn’t have before. These new abilities are hidden and can only be triggered in specific ways. |
Keywords
» Artificial intelligence » Prompting
