Defeated Gemini-1.5-Pro ​​and GPT-4V, ranking among the top three in the world in large model multi-modal capabilities

Recently, Yuncong Technology’s large model has made significant progress in the multi-modal evaluation field of OpenCompass, the authoritative comprehensive evaluation platform. The latest evaluation results show that the average score of Yuncong Technology's Congrong large model in this system is 65.5. This result makes Congrong large model among the top three in the world, surpassing Google's Gemini-1.5-Pro ​​and GPT-4v, second only to On GPT-4o (69.9) and Claude3.5-Sonnet (67.9). In the domestic market, the performance of the large model also exceeded InternVL-Chat (61.7) and GLM-4V (60.8), ranking first.

1. OpenCompass multi-modal list

2. OpenCompass large model open evaluation system is a complete open source and reproducible evaluation framework launched by Shanghai Artificial Intelligence Laboratory.

3. OpenCompass multi-modal evaluation uses 8 representative data sets to objectively quantify the capabilities of multi-modal large models from multiple perspectives, and evaluates dimension coverage:

   • Target detection
   • Text recognition
   • Action Recognition
   • Image Understanding and Relational Reasoning
   • Art and Design
   • Business
   • Science
   • Health and Medicine
   • Humanities and Social Sciences
   • Technology and Engineering
   • Mathematical Reasoning

     

     Figure 2: Calm large model-2.0 Examples of multi-modal capabilities

In this evaluation, the Congrong large model performed well in 6 of the data sets, ranking first in the country (MMbench, MMStar, MathVista, HallusionBench, AI2D, OCRBench), especially in On the OCRBench test set, it achieved the highest score in the world with 827 points (out of 1000 points), and was 13 points higher than the second-place GLM-4v, further improving the ability of the Ronong large model in text recognition, text-centric visual question answering, and document-oriented The applicability in business scenarios such as visual question answering and key information extraction.

Figure 3: Demonstration of OpenCompass China’s large model capabilities

The excellent performance of the Rongrong large model in this system relies on Yuncong Technology’s self-developed efficient multi-modal processing architecture and advanced computing technology to achieve efficient multi-modal Modal data processing capabilities can achieve efficient fusion and switching between visual and language tasks, and maximize the use of computing resources to ensure that high performance and response speed can be maintained when processing large-scale multi-modal data, making the model The training process is more efficient, the convergence speed is faster, and the performance is more stable.

At the same time, it also benefits from Yuncong Technology’s long-term profound accumulation and continuous innovation in the fields of vision and language.模 Figure 4: Calm Model-2.0 Multi-mode Capability Example

Previously, the calm model has refreshed the world record 10 times in the field of vision and cross-modal. , ranking among the top five in the world.

As a platform company focusing on the research and development of human-machine collaboration technology,

Yuncong Technology has been actively promoting the development and application of AI agents and large model technology. With the rapid development of artificial intelligence technology, multi-modal large models have become the core engine driving industrial change. The outstanding performance of the Rongrong model in the OpenCompass large model open evaluation system

is not only a recognition of the technological innovation strength of

Yuncong Technology, but also sets an example in the industry and inspires global technology companies in the new round of artificial intelligence. Reach the heights of competition.

The above is the detailed content of Defeated Gemini-1.5-Pro ​​and GPT-4V, ranking among the top three in the world in large model multi-modal capabilities. For more information, please follow other related articles on the PHP Chinese website!