Small models, big results: Achieving superior intent extraction through decomposition
Topics: LLMO / GEO, Search Intent, User Signals
The document from Google researchers introduces a method to improve “intent extraction”—the ability of an AI to look at a series of user actions on a phone or website and describe the user’s overall goal. While massive AI models are good at this, they are expensive and slow; conversely, small models that can run privately on a phone usually struggle with the complexity. To solve this, the researchers developed a two-stage process where the AI first summarizes every individual click or screen view and then combines those small summaries to figure out the big picture, often outperforming much larger models.
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