Gray-Box Fine-Tuning for Single Backbone Domain Experts
Keywords: Multi-modal Learning, Vision-Language, Foundation Models
TL;DR: We suggest a new framework for domain expert fine-tuning that allows safety and proprietary preserving along with other properties
Abstract: The emergence of foundational models has greatly improved performance across various downstream tasks, with fine-tuning often yielding even better results. However, existing fine-tuning approaches typically require access to model weights and layers, leading to challenges such as managing multiple model copies or inference pipelines, inefficiencies in edge device optimization, and concerns over proprietary rights, privacy, and exposure to unsafe model variants. In this paper, we address these challenges by exploring "Gray-box" fine-tuning approaches, where the model's architecture and weights remain hidden, allowing only gradient propagation. We introduce a novel yet simple and effective framework that adapts to new tasks using two lightweight learnable modules at the model's input and output. Additionally, we present a less restrictive variant that offers more entry points into the model, balancing performance with model exposure. We evaluate our approaches across several backbones on benchmarks for text-image alignment, text-video alignment, and sketch-image alignment. Our results demonstrate that, despite having limited access to the model, our Gray-box approaches achieve competitive performance with full-access fine-tuning methods.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 1850
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