A Concept is More Than a Word: Diversified Unlearning in Text-to-Image Diffusion Models

• URL: http://arxiv.org/abs/2603.18767v1
• Date: Thu, 19 Mar 2026 11:20:40 GMT
• Title: A Concept is More Than a Word: Diversified Unlearning in Text-to-Image Diffusion Models
• Abstract summary: Concept unlearning is a promising direction for reducing the risks of harmful content generation in text-to-image diffusion models.<n>Existing approaches typically rely on keywords to identify the target concept to be unlearned.<n>We propose Diversified Unlearning, a distributional framework that represents a concept through a set of contextually diverse prompts.
• Score: 2.124297073085513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Concept unlearning has emerged as a promising direction for reducing the risks of harmful content generation in text-to-image diffusion models by selectively erasing undesirable concepts from a model's parameters. Existing approaches typically rely on keywords to identify the target concept to be unlearned. However, we show that this keyword-based formulation is inherently limited: a visual concept is multi-dimensional, can be expressed in diverse textual forms, and often overlap with related concepts in the latent space, making keyword-only unlearning, which imprecisely indicate the target concept is brittle and prone to over-forgetting. This occurs because a single keyword represents only a narrow point estimate of the concept, failing to cover its full semantic distribution and entangled variations in the latent space. To address this limitation, we propose Diversified Unlearning, a distributional framework that represents a concept through a set of contextually diverse prompts rather than a single keyword. This richer representation enables more precise and robust unlearning. Through extensive experiments across multiple benchmarks and state-of-the-art baselines, we demonstrate that integrating Diversified Unlearning as an add-on component into existing unlearning pipelines consistently achieves stronger erasure, better retention of unrelated concepts, and improved robustness against adversarial recovery attacks.

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