这篇论文揭示了LLM中一种不易察觉的修辞滥用，并给出了具体测量和缓解方法，适合关注模型文本风格和RLHF影响的读者。
论文发现大语言模型系统性过度使用两千年前的修辞格epanorthosis（如“这不是课程，而是蜕变之旅”），归因于训练数据中促销文案和RLHF偏好调优。作者提出Epanorthosis Index（密度与人类基线之比），在三种规模的指令微调模型上测量：或atory场景下模型超用约2倍（意大利语近3倍），非正式问答中欠用，而议论文、新闻和百科文本与人类持平。缓解方案包括轻量LoRA适配器，在意大利语中单行指令即可削减一半至四分之三，SFT适配器可几乎消除。
Artificial Epanorthosis: Why large language models overuse a classical rhetorical figure, and how to mitigate it
A rhetorical figure that Cicero and Quintilian catalogued two thousand years ago reappears, systematically, in the text of large language models: epanorthosis, the self-correction of the specimen «This is not a course. It is a journey of transformation». This essay argues that the overuse is a trained disposition, driven mainly by a training distribution rich in promotional prose and by preference tuning (RLHF) that rewards confident, emphatic phrasing; the left-to-right nature of generation is an amplifier rather than the root cause. Building on evidence that models diverge from human rhetorical style, and on Fontanier's classification of epanorthosis as a figure of thought, it sets out a programme that scores the figure against genre-specific human baselines through an Epanorthosis Index (density relative to the human rate). A first measurement, on three sizes of one instruction-tuned model family, finds mis-calibration by register in both directions: the models overshoot in oratory (about twofold, near threefold in Italian, concentrated in the larger tiers) and undershoot in informal question-and-answer writing, while matching humans in argument, journalism, and encyclopedic prose. Three constructive contributions follow: a survey of mitigation techniques centred on lightweight LoRA adapters; a demonstration, in Italian, that a one-line instruction cuts the figure by half to nearly three-quarters and that a supervised-fine-tuning adapter removes it almost entirely, with a scaling coefficient that dials the reduction back onto the human rate; and the argument that the target is calibration to the human rate for each genre, not elimination. It closes on the stakes: the real risk is that we begin to write like the machines.