  Modern search engines are built on a stack of different components, including
query understanding, retrieval, multi-stage ranking, and question answering,
among others. These components are often optimized and deployed independently.
In this paper, we introduce a novel conceptual framework called large search
model, which redefines the conventional search stack by unifying search tasks
with one large language model (LLM). All tasks are formulated as autoregressive
text generation problems, allowing for the customization of tasks through the
use of natural language prompts. This proposed framework capitalizes on the
strong language understanding and reasoning capabilities of LLMs, offering the
potential to enhance search result quality while simultaneously simplifying the
existing cumbersome search stack. To substantiate the feasibility of this
framework, we present a series of proof-of-concept experiments and discuss the
potential challenges associated with implementing this approach within
real-world search systems.
