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.

通过统一搜索任务，大规模语言模型框架重新定义了传统的搜索栈，利用其强大的语言理解和推理能力，提高搜索结果质量并简化搜索系统。

大型搜索模型：在 LLMs 时代重新定义搜索架构

Large Search Model: Redefining Search Stack in the Era of LLMs

Denoising Diffusion Probabilistic Models have shown an impressive generation
quality, although their long sampling chain leads to high computational costs.
In this paper, we observe that a long sampling chain also leads to an error
accumulation phenomenon, which is similar to the exposure bias problem in
autoregressive text generation. Specifically, we note that there is a
discrepancy between training and testing, since the former is conditioned on
the ground truth samples, while the latter is conditioned on the previously
generated results. To alleviate this problem, we propose a very simple but
effective training regularization, consisting in perturbing the ground truth
samples to simulate the inference time prediction errors. We empirically show
that the proposed input perturbation leads to a significant improvement of the
sample quality while reducing both the training and the inference times. For
instance, on CelebA 64$\times$64, we achieve a new state-of-the-art FID score
of 1.27, while saving 37.5% of the training time. The code is publicly
available at this https URL

通过加入输入扰动以模拟推理时间预测误差，本文提出了一种简单而有效的训练正则化方法，以减轻 Denoising Diffusion Probabilistic Models 模型中的错误积累现象，并在维持较高的采样质量的同时，显著减少训练和推理时间。