Data Value Estimation for Visual Instruction Tuning: Reducing Redundancy in Multimodal Large Language Models

Core Concepts

Efficiently reduce redundancy in visual instruction datasets for improved performance in multimodal large language models.

Abstract

Visual instruction tuning is crucial for enhancing multimodal large language models (MLLMs). Existing MLLMs rely on diverse visual instruction datasets, leading to data redundancy. A new approach, TIVE, estimates task and instance values to select representative instances, achieving comparable performance with only 7.5% of the data. Experimental results show significant improvements across benchmarks.

Stats

"Using only about 7.5% data can achieve comparable performance as the full-data fine-tuned model."
"Our approach even surpasses the full-data model on four benchmarks."
"With only 16.7% of the data from Vision-Flan dataset, our approach achieves 95% performance compared to the original model."

Quotes

Key Insights Distilled From

Less is More

by Zikang Liu,K... at arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09559.pdf

Deeper Inquiries

Why is it important to consider both task-level and instance-level values in data selection

Considering both task-level and instance-level values in data selection is crucial for several reasons.

Comprehensive Understanding: Task-level values provide an overview of the importance of different tasks within the dataset, helping prioritize tasks that contribute more significantly to model performance. On the other hand, instance-level values offer insights into the significance of individual data samples within each task, ensuring that representative instances are selected for training.

Balanced Data Selection: By combining task-level and instance-level values, a more balanced and informative subset of data can be chosen. This approach ensures that not only are important tasks given appropriate weightage but also that diverse and representative instances within those tasks are included in the selected subset.

Optimized Model Performance: Considering both levels of value allows for a more nuanced selection process, leading to improved model performance during training. By selecting high-value tasks and instances, the model can learn from relevant and impactful data points efficiently.

Reduction of Redundancy: The combination of task-level and instance-level values helps in identifying redundant or less valuable data samples, thereby reducing redundancy in the dataset which can lead to faster convergence during training.

What are the potential implications of reducing redundancy in visual instruction datasets beyond improved model performance

Reducing redundancy in visual instruction datasets has implications beyond just improving model performance:

Efficient Resource Utilization: Eliminating redundant data reduces computational resources required for training models by focusing on essential information only.

Faster Training Times: With reduced redundancy, models can converge faster as they are trained on a more concise and relevant dataset without unnecessary noise or duplicate information.

Enhanced Generalization: Removing redundant data can improve generalization capabilities as models learn from a cleaner dataset with fewer distractions or conflicting information.

Improved Interpretability: A streamlined dataset enhances interpretability as it becomes easier to analyze how specific instructions impact model behavior without interference from irrelevant or duplicated samples.

How can this approach be adapted for other types of multimodal models beyond language processing

This approach can be adapted for other types of multimodal models beyond language processing by customizing the value estimation metrics based on the modalities involved:

For vision-language models: Instead of relying solely on text-based instructions like LLMs do, incorporating image features alongside textual cues would require adapting gradient-based measurements to account for visual input's influence.

2.For speech-language models: Similar principles could apply where audio features play a significant role; hence estimating value based on gradients derived from audio-visual fusion layers would be necessary.
3.For gesture-language models: In scenarios involving gestures along with language inputs, measuring value through joint feature representations could enhance understanding across multiple modalities effectively.
By tailoring this approach to suit various multimodal combinations while considering modality-specific characteristics during value estimation processes will ensure efficient reduction of redundancy across diverse multimodal datasets."