Token Reduction using CLIP Metric (TRIM) is a novel approach designed to enhance the efficiency of Multimodal Large Language Models (MLLMs) by reducing the computational overhead associated with processing image tokens. Inspired by human attention patterns in Visual Question Answering (VQA) tasks, TRIM focuses on selecting and reducing image tokens without compromising the model's performance. The method has been extensively tested across 12 datasets, demonstrating significant reductions in computational requirements while maintaining consistent performance levels. This advancement is crucial for the development of more accessible and sustainable high-performing MLLMs.
CLIP Metric
Multimodal Large Language Model
12 datasets for testing
Computational overhead reduction, Performance consistency
Cloud-based, On-premises
Yes
Yes
Efficient token reduction, Maintains performance
Yes
Standard GPU for model training and inference
Linux, Windows, macOS
Compatible with existing MLLM frameworks
Standard AI model security practices
General AI compliance standards
None
No
Limited community support
Research team from the study
Varies by dataset
Reduced due to token reduction
Improved due to reduced computational requirements
Standard explainability tools for MLLMs
Ensures efficient use of resources
Dependent on the quality of token selection
Technology, AI research
Improving efficiency in VQA tasks
AI researchers, MLLM developers
Integrates with existing MLLM frameworks
Scalable with additional computational resources
Research team support
Standard SLA for AI research projects
Command-line interface
No
Not applicable
Research-based, not commercialized
No
Research collaborations
None
General AI compliance
1.0
Research project
No
Research-based
0.00
Not applicable
Research license
01/12/2023
01/12/2023
+1234567890
Focuses on reducing computational overhead in MLLMs
Yes