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Found 5 papers, 2 papers with code
On Path to Multimodal Generalist: General-Level and General-Bench
This project introduces General-Level, an evaluation framework that defines 5-scale levels of MLLM performance and generality, offering a methodology to compare MLLMs and gauge the progress of existing systems towards more robust multimodal generalists and, ultimately, towards AGI.
Iris: Breaking GUI Complexity with Adaptive Focus and Self-Refining
Digital agents are increasingly employed to automate tasks in interactive digital environments such as web pages, software applications, and operating systems.
Unified Generative and Discriminative Training for Multi-modal Large Language Models
Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval, yet struggles with complex scenarios requiring fine-grained semantic differentiation.
WorldGPT: Empowering LLM as Multimodal World Model
As for evaluation, we build WorldNet, a multimodal state transition prediction benchmark encompassing varied real-life scenarios.
Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions
This shortcoming results in MLLMs' underperformance in comprehending demonstrative instructions consisting of multiple, interleaved, and multimodal instructions that demonstrate the required context to complete a task.
