MAIA: a Benchmark for Multimodal AI Assessment

Abstract

We introduce MAIA (Multimodal AI Assessment), a multimodal dataset developed as a core component of a competence-oriented benchmark designed for fine-grained investigation of the reasoning abilities of Visual Language Models (VLMs) on videos. The MAIA benchmark is characterized by several distinctive features. To the best of our knowledge, MAIA is the first Italian-native benchmark addressing video understanding: videos were carefully selected to reflect Italian culture, and the language data (i.e., questions and reference answers) were produced by native-Italian speakers. Second, MAIA explicitly includes twelve reasoning categories that are specifically designed to assess the reasoning abilities of VLMs on videos. Third, we structured the dataset to support two aligned tasks (i.e., a extit{statement verification} and an extit{open-ended visual question answering}) built on the same datapoints, this way allowing to assess VLM coherence across task formats. Finally MAIA integrates, by design, state-of-the-art LLMs in the development process of the benchmark, taking advantage of their linguistic and reasoning capabilities both for data augmentation and for assessing and improving the overall quality of the data. In the paper we focus on the design principles and the data collection methodology, highlighting how MAIA provides a significant advancement with respect to other available dataset for VLM benchmarking.