Conversational search systems enable information retrieval via natural
language interactions, with the goal of maximizing users' information gain over
multiple dialogue turns. The increasing prevalence of conversational interfaces
adopting this search paradigm challenges traditional information retrieval
approaches, stressing the importance of better understanding the engineering
process of developing these systems. We undertook a systematic literature
review to investigate the links between theoretical studies and technical
implementations of conversational search systems. Our review identifies
real-world application scenarios, system architectures, and functional
components. We consolidate our results by presenting a layered architecture
framework and explaining the core functions of conversational search systems.
Furthermore, we reflect on our findings in light of the rapid progress in large
language models, discussing their capabilities, limitations, and directions for
future research.

通过对对话式搜索系统进行系统性文献回顾，我们识别出了真实世界的应用场景、系统架构和功能组件，给出了一个分层架构框架，并解释了对话式搜索系统的核心功能。此外，我们讨论了大型语言模型的能力、局限性和未来研究方向。

构建对话搜索系统：应用、架构和功能组件综述

Engineering Conversational Search Systems: A Review of Applications,  Architectures, and Functional Components

Understanding and characterizing how people interact in information-seeking
conversations is crucial in developing conversational search systems. In this
paper, we introduce a new dataset designed for this purpose and use it to
analyze information-seeking conversations by user intent distribution,
co-occurrence, and flow patterns. The MSDialog dataset is a labeled dialog
dataset of question answering (QA) interactions between information seekers and
providers from an online forum on Microsoft products. The dataset contains more
than 2,000 multi-turn QA dialogs with 10,000 utterances that are annotated with
user intent on the utterance level. Annotations were done using crowdsourcing.
With MSDialog, we find some highly recurring patterns in user intent during an
information-seeking process. They could be useful for designing conversational
search systems. We will make our dataset freely available to encourage
exploration of information-seeking conversation models.

本研究介绍了一个新的数据集，用于分析信息搜寻对话的用户意图分布、共同出现和流程模式，并利用 MSDialog 数据集发现了一些可用于设计对话式搜索系统的高频用户意图模式。