大模型过度使用自我修正修辞,导致文本风格失真。
Artificial Epanorthosis: Why large language models overuse a classical rhetorical figure, and how to mitigate it
- 通过分析训练数据与偏好优化,揭示修辞过量的根源
- 发现模型在演讲类文本中修辞密度达人类两倍以上
- 提出基于领域基准的校准方案,适配不同文体需求
一种两千年前就被西塞罗和昆提利安记录的修辞手法——自我修正(epanorthosis),在大型语言模型的文本中系统性重现。本文指出,这种过度使用是训练数据中大量宣传性文本与偏好调优(RLHF)共同作用的结果,而非自左至右生成机制本身。基于福坦尼耶对修辞分类的理论,提出以「自我修正指数」衡量模型在不同语体中与人类基准的偏差。对三个尺寸的指令微调模型家族进行测量发现:模型在演说类文本中修辞密度约为人类的两倍(意大利语接近三倍),且集中于大模型层级;而在非正式问答中则显著低于人类。随后提出三项改进:轻量级LoRA适配器缓解策略、一条指令可使意大利语中该修辞减少一半至四分之三、监督微调适配器几乎完全消除该现象,并可通过缩放系数精准校准至人类水平。最终强调,目标不是彻底消除该修辞,而是根据不同语体实现与人类一致的风格校准。文章警示:真正的风险是我们开始像机器一样写作。
原文摘要 · Abstract (English)
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.
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