Towards Conversational Dataset Retrieval: A Survey
TL;DR
A novel, layered conceptual framework is introduced that organizes research in CDR across four key dimensions: User Layer, System Layer, Data Layer, and Evaluation Layer and identifies core challenges in CDR, including the lack of standardized evaluation benchmarks and limited support for ambiguous or evolving user intent.
Abstract
Large language models (LLMs) have sparked renewed interest in Conversational Information Retrieval (CIR). Within this shift, Conversational Dataset Retrieval (CDR) is emerging as a new subfield that focuses on using natural, context-aware dialogue to discover structured and semi-structured datasets. We present the first integrative review of this rapidly evolving landscape. We synthesise insights from 44 publications spanning user studies, conversational system design choices, dataset representation and access, and evaluation methods. We introduce a novel, layered conceptual framework that organizes research in CDR across four key dimensions: User Layer, System Layer, Data Layer, and Evaluation Layer. This framework is used to highlight recurring design patterns, technical advances, and persistent gaps. Our analysis identifies core challenges in CDR, including the lack of standardized evaluation benchmarks and limited support for ambiguous or evolving user intent. The aim of this survey is to provide a structured foundation for future research on CDR and guide the development of more interactive, intelligent, and user-centric dataset retrieval systems.