Engineering Conversational Search Systems: A Review of Applications, Architectures, and Functional Components (2024)

Abstract: 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.

## introduction accessing information has always been one of the primary functions of computer systems. early systems relied on command-line interfaces with a specific syntax for data retrieval. as search systems evolved, database query languages enabled more complex queries but required technical knowledge. then, free-text search engines allowed users to enter keywords in natural language, with information typically displayed as a result page listing relevant items @xcite . in recent years, the evolution of search systems has continued in the direction of human-like dialogues. conversational search has emerged as a novel search paradigm, marking a shift from traditional search engines to interactive dialogues with intelligent agents @xcite . many people have grown accustomed to using conversational interfaces like chatbots and voice assistants @xcite . the widespread usage of dialogue systems has changed how humans expect to interact with computers @xcite . although modern conversational agents have impressive skill sets, their information-seeking capabilities are relatively limited and often confined to answering simple questions. as a consequence, there is a growing research interest in developing conversational search interfaces that go beyond simple query-response interactions by supporting more complex mixed-initiative dialogues, which is further fueled by the surging popularity of large language models (llms) and their integration into many kinds of search applications. even though the topic of conversational search is relatively new, its fundamental concepts can be traced back to early works from the natural language processing (nlp) and information retrieval fields. so far, this emerging topic has been approached from different angles. while some researchers focus on theories and conceptual aspects @xcite , others conduct dialogue analyses and build prototypes to ground abstract models in empirical studies @xcite . yet, despite the ample literature about required properties, many proposed systems are too complex to implement. this apparent gap highlights the need for a more holistic inspection that connects theoretical requirements with realizable functional components. we conducted a systematic literature review investigating different aspects of conversational search systems (csss) to address this research gap. the three main contributions are as follows: ## related work in the related research literature on systems for conversational information-seeking, three categories are usually distinguished: search, recommendation, and question-answering (qa) @xcite . as the name suggests, csss actively involve users in the search process. through multi-turn dialogues, users enter queries, locate information, examine results, or refine their search goals. in contrast to search systems, recommender systems usually rely on data about user preferences and past interaction histories to help with decision-making by providing personalized recommendations. qa systems have been an active area of research for many decades. given a text corpus or knowledge base and a dialogue history, conversational qa systems aim to find answers to natural language questions @xcite . it is worth noting that the boundaries between conversational search, recommender, and qa systems are blurred and overlap. although surveys exist on the two latter system categories @xcite , our literature review is dedicated to search-oriented conversational interfaces. despite the growing body of research on conversational search, related work, such as surveys or systematic literature reviews, remains scarce. the few studies we found tend to have a narrow topic focus on certain application domains or challenges. for example, the survey from @xcite gives an overview of conversational search applications in biomedicine. in a different study, @xcite address the challenge of dealing with ambiguous queries. another literature study from @xcite investigates problematic biases in personalized content that conversational search agents can exhibit. yet another work by @xcite is a comprehensive survey on meta-information in search-oriented conversations. to the best of our knowledge, we are the first to provide a system-centric review across the development process, ranging from conceptualizing core functions to implementing architectural components. unlike the mentioned studies, we do not look into specific challenges or domains within conversational search but take on a broad engineering perspective. we summarize valuable insights regarding the design and development of csss for several application use cases. additionally, we address the recent interest surrounding llms and their potential implications for engineering csss. ## method we conducted our systematic review based on the guidelines from @xcite . our study aims to shed light on the complex engineering process behind csss from initial system requirements to technical implementations by focusing on three key aspects: (1) definitions and proposed application scenarios to conceptualize the functional requirements of csss, (2) architectural elements suggested in the literature to effectively support these required system properties, and (3) core functions of csss discussed in the academic literature along with their implementations. to obtain relevant publications, we devised a search string for querying six academic databases, as presented in ## discussion and future directions the results from our review give insights into the engineering behind csss from abstract properties to realizable functional components. against this background, our findings unveil a disruptive trend of adopting larger language models to integrate end-to-end functional components. researchers have emphasized the benefits of streamlined nlp, reduced error propagation, and data-driven development. hence, rather than reflecting on the numerous general challenges in the evaluation of csss, like penha and hauff (2020), we direct our focus toward discussing how llms can augment csss and the implications it has on their future evolution. while most studies fine-tune language models (e.g., bert or t5) on downstream tasks, there has been a recent surge of interest in using llms. by scaling up models to billions of parameters and training them on corpora with trillions of tokens, llms have demonstrated emergent capabilities and prowess in multi-task learning @xcite . a significant advantage of llms is promptbased (or in-context) learning. through carefully defined prompts, llms can perform multiple tasks without specific training or tuning @xcite . furthermore, there has been a growing interest in optimizing llms for dialogue interactions by pretraining on conversations, instruction fine-tuning, and reinforcement learning from human feedback @xcite . the strengths of llms, such as their language understanding and ability to generate context-aware responses, make them highly complementary elements for csss. ## conclusion we conducted a comprehensive review of engineering csss, establishing connections between theoretical application scenarios and technical im-plementations. based on our analysis of existing architectures, we introduced a layered architecture framework and explained its functional core components. while it is essential to acknowledge that the field of conversational search is rapidly evolving, and complete coverage is unattainable, our framework provides a generalized architecture based on previously validated systems. the framework does not claim to be exhaustive but rather serves as a foundational starting point for designing and developing csss. lastly, we discussed recent work on the capabilities and challenges of augmenting csss with llms. we outline where they fit into our proposed framework, which core functions they have been used for, and highlight promising directions for future research.