The rise of LLMs has been a game-changer for search. These models enable us to better understand complex human language queries, extract rich semantic meaning from content, and generate more relevant and context-aware results. Chen and his team are exploring how to integrate LLMs like Llama into the Facebook search ranking stack by building high-quality pretrained models, fine-tuning LLMs with few-shot learning and leveraging prompt engineering with LLM capabilities. Not only are LLMs enhancing the semantic relevance, location, freshness and quality of search results, they’re also evaluating the end-to-end (E2E) ranking stack to deliver more personalized responses.
Transforming search with LLMs: From keywords to understanding
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