Recent advances in Large Language Models (LLMs) have initiated a fundamental shift: from purely text-based language models to Large Multimodal Models (LMMs). These models combine multiple input channels—text, image, audio, and video—unlocking new dimensions in the analysis and generation of media content.
A special case are Visual Language Models (VLMs), which combine image and text information to enable deeper image and video analysis. The increasing performance of these multimodal systems is transforming the way companies access, interpret, and leverage data for business processes.
From LLMs to LMMs: The Technological Evolution
- Early LLMs: Optimized for text, focused on language understanding and generation.
- Transition to Multimodality: Starting with specific versions, LLMs became capable of processing visual or auditory data.
- Specialization through VLMs: These models focus on image and video analysis, combining semantic text understanding with visual pattern recognition.
However, performance depends heavily on model type, architecture, and training dataset. While some models excel in text comprehension, others deliver superior results in image classification or multimodal retrieval tasks.
Model Selection: Strengths, Weaknesses, and Requirements
Selecting the right model is not a standard decision—it requires:
- Systematic Benchmarking: Only reproducible evaluations reveal actual strengths and weaknesses.
- Version and Release Management: Performance differences between model generations can be significant.
- Use-Case-Specific Requirements: A model strong in text generation may not be optimal for video analysis or audio understanding.
To support this, sol4data provides an evaluation matrix that connects technical metrics (accuracy, latency, robustness) with use-case requirements.
sol4data: Expertise in Multimodal AI
Our role as a Generative AI specialist is built on a three-step approach:
- Market Screening & Model Evaluation – continuous analysis of new LLM and LMM releases.
- Technological Due Diligence – assessment of scalability, integration capabilities, and performance.
- Use-Case Mapping – translating model capabilities into productive architectures for our clients.
Our consultants bring decades of experience in data management, machine learning, and AI infrastructure—as long as these fields have existed. This combination enables us not only to understand the technology but also to apply it effectively in a business context.
Example: Media Analysis with Multimodal Models
Today, LMMs enable complex analyses and generations that were impossible just a few years ago:
- Automated Object Recognition & Labeling – media assets can be enriched with semantic tags.
- Visual Similarity Analysis – comparison and clustering of images based on content and aesthetics.
- Aesthetic and Impact Prediction – models can forecast how certain images will resonate with target groups.
- Cultural and Demographic Correlation – media can be mapped to age groups, cultural contexts, or markets.
These capabilities form the foundation for Advanced Media Analytics, empowering companies not only to manage but also to strategically leverage media content.
Conclusion
The evolution from LLMs to LMMs and VLMs marks a decisive step in the development of artificial intelligence. Multimodality is not hype but a technological necessity to address the diversity of today’s data formats.
sol4data provides enterprises with the technological depth, evaluation frameworks, and expertise to select and successfully integrate the best models for productive media analytics.
This lays the foundation for scalable, precise, and value-driven Advanced Media Analytics with AI.
