通过递归重构符号结构，实现对大模型表示的可控扰动。
Structural Perturbation in Large Language Model Representations through Recursive Symbolic Regeneration
- 递归再生符号结构，生成有组织的潜在表征变化。
- 扰动后注意力分布改变，提升长文本生成的多样性与一致性。
- 无需重训练即可调整模型行为，适合领域适配与风格控制。
符号扰动提供了一种无需修改模型参数即可影响神经表征的新方法。通过递归再生符号结构，引入了潜在嵌入中的结构性变化，导致注意力动态和序列生成中词汇多样性的可控调整。与传统微调相比，符号层面的结构修改在保持整体语言流畅性与连贯性的前提下，引发了不同的上下文敏感性变化。注意力权重分布的变化表明，符号修改可调节标记依赖关系，影响响应变异性，并优化长文本生成。实验表明，符号扰动可增强模型在特定领域的适应性，实现不需重新训练的行为调整。语义漂移评估显示，递归再生改变了长程标记依赖，影响长文本序列的主题连贯性。词汇多样性评估结果进一步支持：符号级修改能引入可解释的生成差异，或可用于更受控的自动文本风格调整。
原文摘要 · Abstract (English)
Symbolic perturbations offer a novel approach for influencing neural representations without requiring direct modification of model parameters. The recursive regeneration of symbolic structures introduces structured variations in latent embeddings, leading to controlled shifts in attention dynamics and lexical diversity across sequential generations. A comparative analysis with conventional fine-tuning techniques reveals that structural modifications at the symbolic level induce distinct variations in contextual sensitivity while maintaining overall model fluency and coherence. Shifts in attention weight distributions highlight the role of symbolic modifications in adjusting token dependencies, influencing response variability, and refining long-form text generation. Experimental findings suggest that symbolic perturbations can enhance adaptability in domain-specific applications, allowing modifications in model behavior without retraining. Evaluations of semantic drift indicate that recursive regeneration alters long-range token dependencies, affecting topic coherence across extended text sequences. Results from lexical variability assessments further support the conclusion that symbolic-level modifications introduce interpretable variations in generated responses, potentially enabling more controlled stylistic adjustments in automated text generation.
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