MMLongCite: A Benchmark for Evaluating Faithfulness of Long-Context Vision-Language Models
MMLongCite is a new benchmark created to test how well large vision-language models (LVLMs) handle long contexts across different types of data like text, images, and videos. It features eight diverse tasks spanning six context lengths to check if these models can provide accurate citations and correct answers when dealing with extensive multimodal information.