analiza aiqbench
This arXiv survey reviews how multimodal large language models perform across text, vision, and audio tasks, and compares their strengths and weaknesses by task. Its most useful contribution for practitioners is the catalogue of current shortcomings and open research directions, which can guide evaluation and development priorities. As a survey, it synthesizes existing work rather than presenting new benchmark results, so readers should check its claims against the underlying primary papers.
A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks
arXiv:2408.01319v2 Announce Type: replace Abstract: In an era defined by the explosive growth of data and rapid technological advancements, Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence (AI) systems. Designed to seamlessly integrate diverse data types-including text, images, videos, audio, and physiological sequences-MLLMs address the complexities of real-world applications far beyond the capabilities of single-modality systems. In this paper, we systematically sort out the applications of MLLM in multimodal tasks such as natural language, vision, and audio. We also provide a comparative analysis of the focus of different MLLMs in the tasks, and provide insights into the shortcomings of current MLLMs, and suggest potential directions for future research. Through these discussions, this paper hopes to provide valuable insights for the further development and application of MLLM.
Abstract:In an era defined by the explosive growth of data and rapid technological advancements, Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence (AI) systems. Designed to seamlessly integrate diverse data types-including text, images, videos, audio, and physiological sequences-MLLMs address the complexities of real-world applications far beyond the capabilities of single-modality systems. In this paper, we systematically sort out the applications of MLLM in multimodal tasks such as natural language, vision, and audio. We also provide a comparative analysis of the focus of different MLLMs in the tasks, and provide insights into the shortcomings of current MLLMs, and suggest potential directions for future research. Through these discussions, this paper hopes to provide valuable insights for the further development and application of MLLM.
Artificial Intelligence (cs.AI)
arXiv:2408.01319 [cs.AI]
(or arXiv:2408.01319v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2408.01319 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 217 cuvinte
Subiecte conexe
A complementary study on PlanGPT: Evaluation with defined Performance Metrics and comparison with a planner
arXiv · 10 iun. 2026
FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes
arXiv · 14 oct. 2026
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
arXiv · 25 sept. 2026
Hybrid Gated Attention
arXiv · 15 oct. 2026