POINTS: Improving Your Vision-language Model with Affordable Strategies (2409.04828v3)

Abstract: In recent years, vision-LLMs have made significant strides, excelling in tasks like optical character recognition and geometric problem-solving. However, several critical issues remain: 1) Proprietary models often lack transparency about their architectures, while open-source models need more detailed ablations of their training strategies. 2) Pre-training data in open-source works is under-explored, with datasets added empirically, making the process cumbersome. 3) Fine-tuning often focuses on adding datasets, leading to diminishing returns. To address these issues, we propose the following contributions: 1) We trained a robust baseline model using the latest advancements in vision-LLMs, introducing effective improvements and conducting comprehensive ablation and validation for each technique. 2) Inspired by recent work on LLMs, we filtered pre-training data using perplexity, selecting the lowest perplexity data for training. This approach allowed us to train on a curated 1M dataset, achieving competitive performance. 3) During visual instruction tuning, we used model soup on different datasets when adding more datasets yielded marginal improvements. These innovations resulted in a 9B parameter model that performs competitively with state-of-the-art models. Our strategies are efficient and lightweight, making them easily adoptable by the community.

• The paper presents a robust POINTS model that integrates methods like DHR, CATTY, and dual vision encoders to improve OCR and image-text tasks.
• The paper refines the pre-training dataset using perplexity filtering to select the top 20% of data, boosting performance with a curated 1-million sample subset.
• The paper introduces a Model Soup approach that merges fine-tuned models, with the Greedy Soup variant delivering significant gains across benchmarks such as OCRBench.

Insightful Overview of POINTS: Enhancing Vision-LLMs with Affordable Strategies

The paper "POINTS: Improving Your Vision-LLM with Affordable Strategies" introduces a series of methodological refinements to enhance the performance of vision-LLMs (VLMs). Current VLMs have made impressive strides in tasks such as optical character recognition (OCR) and geometric problem-solving, yet face ongoing challenges related to architectural transparency, efficient pre-training data usage, and diminishing returns during fine-tuning. The authors propose a robust solution in the form of the POINTS model, which integrates several cost-effective strategies to address these problems comprehensively.

Contributions and Methodologies

Strong Baseline Establishment

The authors first establish a robust baseline incorporating contemporary innovations in VLMs. They integrate several cutting-edge techniques and components:

1. Dynamic High Resolution (DHR): This method splits high-resolution images into smaller tiles to be processed by standard vision encoders, preserving fine-grained details and reducing hallucination.
2. Consistent Aspect Ratio Dynamic High Resolution (CATTY): An enhancement over DHR, CATTY maintains the aspect ratios of images to avoid distortions, thus improving OCR capabilities.
3. CapFusion: Merges raw and synthetic captions using a LLM to balance real-world knowledge and grammatical structure.
4. Dual Vision Encoder (DVE): Combines features from a general vision encoder and an OCR-specific encoder, augmenting text extraction from images.
5. Individual Select (IS): Curates the most effective datasets for visual instruction tuning through iterative selection.

These refinements demonstrate significant improvements across various tasks and benchmarks, substantiating the utility of each component.

Pre-training Dataset Refinement

The paper highlights inefficiencies in existing pre-training datasets and introduces perplexity-based filtering. By selecting the top 20% of data with the lowest perplexity values, the authors curate a high-quality 1-million data subset. This filtering not only enhances model performance by focusing on more informative data but also mitigates the issue of training on obscure and less beneficial samples.

Optimized Visual Instruction Tuning with Model Soup

The most significant innovation is the use of Model Soup—a technique where models fine-tuned on different datasets are merged to achieve superior performance. When the addition of new datasets no longer yields significant benefits through traditional methods, model soup provides a substantial performance boost. The authors explore various strategies, including Maximum Soup, Average Soup, and Greedy Soup, with Greedy Soup delivering impactful results across different evaluation metrics.

Numerical Results

The POINTS model, comprising 9 billion parameters, outperforms several state-of-the-art models in benchmarks such as MMBench, MathVista, and OCRBench. Notably, it achieves a score of 69.2% on OCRBench, demonstrating its enhanced text extraction capabilities. The use of a smaller pre-training dataset (1M) filtered by perplexity yields an overall performance increase from 59.0 to 59.6, showcasing the efficacy of this approach.

Implications and Future Directions

The contributions of this paper have significant implications for both practical applications and theoretical advancements in AI:

• Efficiency Improvements: The proposed methods enable more efficient use of computational resources by reducing the need for extensive data and complex processing pipelines.
• Performance Gains: The integration of DHR, CATTY, and DVE notably boosts the model's visual and textual processing capabilities, relevant for applications requiring detailed image analysis and text extraction.
• Scalable Techniques: The methodologies, including perplexity filtering and model soup, provide scalable strategies that can be easily adopted by the wider research community.

Future developments in AI could leverage these innovations to explore further optimizations, such as adaptive data filtering based on ongoing model training performance and dynamic re-weighting of model components based on task-specific needs.

Overall, the POINTS model presents a structured and efficient approach to enhancing vision-LLMs, addressing existing challenges with thoughtful and innovative strategies. These contributions are likely to influence ongoing and future research in multimodal AI systems, offering practical solutions that balance performance and computational affordability.