A key challenge in AI is enabling systems to learn from just a few examples, like humans can. One technique that helps is showing the AI systems answered examples to guide its reasoning, called demonstration learning.
New research from Meituan in China aims to strengthen how well AI models leverage demonstration examples.
Their key ideas:
- Use a mechanism to highlight the most relevant example for each new question.This focuses the system's attention on the most applicable knowledge.
- Test if the system can recall the answers from each example. This forces more thorough learning of the demonstration data.
- Together these enhance how well the AI associates new questions with prior examples.
In experiments, the new technique improved the AI's ability to classify questions after seeing just a few examples. It also directed the model's attention more towards the demonstration data.
Why it matters:
Getting AI to deeply learn from small datasets, like humans can, is a fundamental challenge. Demonstration learning is a promising technique towards this goal.
This research provides insights into why existing methods underperform, and a blueprint to strengthen example-based learning in AI systems.
The capability to efficiently leverage knowledge from limited data has immense potential to make AI systems more flexible, nimble and scalable.
In summary, this work moves towards more human-like learning from examples - a small but crucial step on the path to more general and adaptable artificial intelligence.
Sources:
