Open Domain Question Answering (ODQA) within natural language processing involves building systems that answer factual questions using large-scale knowledge corpora. Recent advances stem from the confluence of several factors, such as large-scale training datasets, deep learning techniques, and the rise of large language models. High-quality datasets are used to train models on realistic scenarios and enable the evaluation of the system on potentially unseen data. Standardized metrics facilitate comparisons between different ODQA systems, allowing researchers to objectively track advancements in the field. Our study presents a thorough examination of the current landscape of ODQA benchmarking by reviewing 52 datasets and 20 evaluation techniques across textual and multimodal modalities. We introduce a novel taxonomy for ODQA datasets that incorporates both the modality and difficulty of the question types. Additionally, we present a structured organization of ODQA evaluation metrics along with a critical analysis of their inherent trade-offs. Our study aims to empower researchers by providing a framework for the robust evaluation of modern question-answering systems. We conclude by identifying the current challenges and outlining promising avenues for future research and development.

本研究通过对52个数据集和20种评估技术进行综述，详细研究了当前开放域问题回答领域的现状，提出了一种包含问题类型的多模态数据集新分类法，并对评估指标进行结构化整理和批判性分析，旨在为现代问答系统的强大评估提供框架，并指出了当前的挑战和未来研究发展的有希望的方向。

朝向稳健评估：大语言模型时代开放领域问答数据集和度量的综合分类