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In an age characterized by the vast and ever-expanding wealth of information available on the internet, search engines have become an indispensable tool for the discovery and retrieval of knowledge. To harness the full spectrum of valuable information offered by Search Engine Result Pages (SERPs), including direct answers, featured snippets, knowledge panels, related queries, multimedia content, and more, modern search engines are built upon a multifaceted foundation. This foundation comprises a multitude of components, such as query understanding, retrieval, multi-stage ranking, and question answering.
Traditionally, these components have been designed and fine-tuned independently, typically involving the optimization of pre-trained language models like BERT or T5 using task-specific datasets. This approach, however, places substantial demands on resources and manpower, resulting in high overheads. As such, there is a growing need for a more unified modeling framework that provides flexible interfaces and enhanced generalization.
In response to this demand, in a new paper Large Search Model: Redefining Search Stack in the Era of LLMs, a Microsoft research team presents a novel conceptual framework, large search model, which reimagines the conventional search stack by consolidating various search tasks under a single Large Language Model (LLM). Leveraging the robust language understanding and reasoning capabilities of LLMs, this approach holds the promise of improving the quality of search results while streamlining the often intricate and cumbersome search stack.
The traditional search stack is characterized by a cascading retrieval and ranking pipeline, along with a multitude of other components that collectively generate the SERP. In contrast, the proposed framework adopts a unified modeling strategy that relies on prompts to tailor the large search model for diverse search tasks. In essence, the research team defines the large search model as a customized LLM (which may incorporate multimodal capabilities) capable of robustly performing a variety of Information…