VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language Models (2412.01822v1)

Abstract: The recent surge in high-quality visual instruction tuning samples from closed-source vision-LLMs (VLMs) such as GPT-4V has accelerated the release of open-source VLMs across various model sizes. However, scaling VLMs to improve performance using larger models brings significant computational challenges, especially for deployment on resource-constrained devices like mobile platforms and robots. To address this, we propose VLsI: Verbalized Layers-to-Interactions, a new VLM family in 2B and 7B model sizes, which prioritizes efficiency without compromising accuracy. VLsI leverages a unique, layer-wise distillation process, introducing intermediate "verbalizers" that map features from each layer to natural language space, allowing smaller VLMs to flexibly align with the reasoning processes of larger VLMs. This approach mitigates the training instability often encountered in output imitation and goes beyond typical final-layer tuning by aligning the small VLMs' layer-wise progression with that of the large ones. We validate VLsI across ten challenging vision-language benchmarks, achieving notable performance gains (11.0% for 2B and 17.4% for 7B) over GPT-4V without the need for model scaling, merging, or architectural changes.

• The paper proposes a layer-wise distillation technique that verbalizes intermediate features to transfer knowledge from large to small VLMs.
• It employs adaptive layer matching and reinforcement training, achieving performance gains of 11.0% to 17.4% on various benchmarks.
• The approach enables efficient deployment of high-performing VLMs in resource-constrained settings without increasing model size.

Verbalized Layers-to-Interactions: Advancing Vision LLMs

The paper, "VLsI: Verbalized Layers-to-Interactions from Large to Small Vision LLMs," introduces a novel methodology for enhancing vision-LLMs (VLMs) by proposing an efficient knowledge distillation technique. This work addresses the computational bottleneck associated with scaling VLMs for deployment in resource-constrained environments, such as mobile and robotic platforms. By leveraging NLP techniques within the layer-wise distillation process, the authors aim to align the reasoning capabilities of smaller VLMs with their larger counterparts without necessitating structural alterations or increases in model size.

Core Innovations and Methodology

VLsI introduces a unique layer-wise distillation process that employs verbalizers—intermediate digital entities that transform layer-specific features into natural language expressions. This design strategy facilitates a smoother knowledge transfer from large to small VLMs, circumventing the issues typical of conventional distillation methods, such as instability in output imitation. This layer-wise progression method ensures that each layer in a smaller VLM is progressively aligned with the reasoning capabilities of its larger counterpart.

The proposed methodology is broken into three critical training steps: verbalization, interaction, and reinforcement. During the verbalization step, verbalizers project intermediate features into the language space, making the outputs interpretable as text-based responses. The interaction step uses adaptive layer matching, aligning layers between large and small VLMs dynamically. Finally, the reinforcement step finetunes the distilled VLMs to enhance task-specific responsiveness, ensuring that the distilled model retains high-performance standards.

Empirical Evaluation

Experiments conducted across a diverse array of benchmarks validate VLsI's efficacy. Notably, the proposed VLsI models demonstrate an improvement of 11.0% for the 2B model and 17.4% for the 7B model over the baseline GPT-4V without increasing the model size. Such gains underscore the capability of VLsI to enhance performance while maintaining computational efficiency. Additionally, these results highlight the robustness of the VLsI approach across multiple tasks, indicating its adaptability and practicality in various application settings.

Theoretical and Practical Implications

VLsI presents significant implications both theoretically and practically. Theoretically, it advances the understanding of how natural language representations can be integrated into traditional VLM structures to facilitate efficient knowledge transfer. It demonstrates the potential for NLP techniques to enhance model interpretability and alignment without relying on increased model complexity. Practically, VLsI's approach opens pathways for deploying high-performing VLMs in environments where computational resources are limited, thereby broadening the scope of applications in real-world scenarios.

Future Directions

Future research could explore expanding this framework to support a broader range of vision-language tasks and incorporating diverse model architectures that use varying tokenizers or index orders. Further investigation into optimizing the verbalization framework may also yield insights into more generalized applications of this distillation strategy, thereby enhancing the flexibility and scalability of VLMs in ever-evolving AI ecosystems.

In conclusion, this work presents a compelling advancement in vision-LLMing, demonstrating both the theoretical and practical viability of deep integration between VLMs and natural language processing. By focusing on layer-wise verbalization and interaction, VLsI offers a novel avenue for efficient VLM development, promising enhanced performance without the accompanying computational costs traditionally associated with model scaling.