What is the impact of noise injection for differential privacy on the convergence stability of multimodal LLMs

Abstract

Abstract: Large language models (LLMs) can handle a wide variety of general tasks with simple prompts, without the need for task-specific training. Multimodal Large Language Models (MLLMs), built upon LLMs, have demonstrated impressive potential in tackling complex tasks involving visual, auditory, and textual data. However, critical issues related to truthfulness, safety, o1-like reasoning, and alignment with human preference remain insufficiently addressed. This gap has spurred the emergence of various alignment algorithms, each targeting different application scenarios and optimization goals. Recent

Research Question

What is the impact of noise injection for differential privacy on the convergence stability of multimodal LLMs during few-shot image-text alignment tasks?

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