为何基于指令的反学习在扩散模型中失效？
Why Instruction-Based Unlearning Fails in Diffusion Models?
AI总结：
研究探讨了基于指令的反学习在扩散模型中的有效性，发现扩散模型在仅依赖自然语言反学习指令时无法有效抑制目标概念，揭示了提示级指令的局限性。
摘要：
基于指令的反学习已证明在修改大语言模型推理行为时有效，但其在其他生成模型中的适用性尚不明确。本文研究了扩散模型中的基于指令的反学习，通过多个概念和提示变体的受控实验，显示扩散模型在仅受自然语言反学习指令引导时系统性地无法抑制目标概念。通过分析CLIP文本编码器和去噪过程中交叉注意力动态，发现反学习指令未导致对目标概念标记的持续注意力降低，致使目标概念表示在整个生成过程中持续存在。这些结果揭示了扩散模型中提示级指令的根本限制，并表明有效的反学习需要超越推理时间语言控制的干预。
英文摘要：
Instruction-based unlearning has proven effective for modifying the behavior of large language models at inference time, but whether this paradigm extends to other generative models remains unclear. In this work, we investigate instruction-based unlearning in diffusion-based image generation models and show, through controlled experiments across multiple concepts and prompt variants, that diffusion models systematically fail to suppress targeted concepts when guided solely by natural-language unlearning instructions. By analyzing both the CLIP text encoder and cross-attention dynamics during the denoising process, we find that unlearning instructions do not induce sustained reductions in attention to the targeted concept tokens, causing the targeted concept representations to persist throughout generation. These results reveal a fundamental limitation of prompt-level instruction in diffusion models and suggest that effective unlearning requires interventions beyond inference-time language control.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
