SIEVE: New Method Enables Language Models to Learn from Just 3 Examples

Researchers introduced SIEVE, a technique for efficiently adapting language models through parametric learning using minimal natural language examples. The method decomposes context and generates synthetic training data, outperforming prior approaches on reasoning tasks with only three query examples, potentially reducing AI training costs.