The Landscape of Multimodal Evaluation Benchmarks

The blog post explores the evolving landscape of multimodal datasets used to evaluate large language models (LLMs) that process multiple input types, such as text and images. It highlights various datasets and benchmarks, like TextVQA and DocVQA, which focus on optical character recognition (OCR) and visual question answering (VQA), and more specialized ones like MathVista for mathematical reasoning and LogicVista for logical reasoning. The post notes the trend toward creating curated collections of samples for comprehensive evaluation due to the growing number of datasets and the risk of model overfitting to specific benchmarks. It also mentions the importance of scalable infrastructure for deploying and running models, featuring Clarifai's Compute Orchestration as a solution for maintaining control over performance and costs across different cloud environments.