Towards Robust Evaluation: A Comprehensive Taxonomy of Datasets and Metrics for Open Domain Question Answering in the Era of Large Language Models

TL;DR

This paper provides a comprehensive review and taxonomy of datasets and evaluation metrics for open domain question answering, aiming to improve the robustness and comparability of system assessments in the era of large language models.

Contribution

It introduces a novel taxonomy for ODQA datasets based on modality and difficulty, and offers a structured organization and critical analysis of evaluation metrics.

Findings

Reviewed 52 datasets and 20 evaluation techniques

Proposed a taxonomy incorporating modality and question difficulty

Analyzed trade-offs in current evaluation metrics

Abstract

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.