Query fan-out optimization has a simple goal: making your content useful across multiple related queries at once, not just a single keyword. AI-powered search systems and LLMs increasingly answer ...

Enterprise AI has a data problem. Despite billions in investment and increasingly capable language models, most organizations still can't answer basic analytical questions about their document ...

Large Language Models (LLMs) are recasting the relationship between humans & technology. There’s a complete transition in how we search, consume, and execute information on the web. LLMs are no longer ...

Learning complex, detailed, and evolving knowledge is a challenge in multiple technical professions. Relevant source knowledge is contained within many large documents and information sources with ...

Google’s query fan-out technique issues multiple background searches based on initial question. This system is active across AI Mode, Deep Search, and some AI Overview results. The approach relies on ...

Large language models (LLMs) show intriguing human-like behaviors despite being trained solely via language prediction. Are these models developing human-like concepts central to human understanding?

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Abstract: Query rewriting aims to generate a new query that can complement the original query to improve the information retrieval system. Recent studies on query rewriting, such as query2doc, ...

Domains like social media analysis, e-commerce, and healthcare data management require querying through large chunks of structured and unstructured databases. In this modern world, there has been an ...