时间胶囊：将生成性幻觉作为历史意义建构的一种方法
TimeCapsule: Generative Hallucination as a Method for Historical Sensemaking
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中文总结 AI 辅助
研究针对大语言模型在叙述过去时不可靠的问题，提出在维多利亚时代文本上训练的TimeCapsule模型，通过定量评估和定性探究展示其性能及问题，认为对未来的无知使幻觉成为对19世纪本体论的解释性探索。
中文摘要 AI 辅助
大语言模型在时间上过度暴露，在大量当代语料库上训练，编码的当今概念使其成为过去不可靠的叙述者。我们提出了TimeCapsule，一个有12亿参数的类似LLaMA的因果模型，专门在维多利亚时代文本（1800 - 1875）上训练，作为一个认识论上孤立的生成性存档。定量评估显示，在保留的维多利亚时代散文上，相对于GPT - 2基线，困惑度降低了45.4%。TimeCapsule展现出计算意义建构能力，能为不熟悉的现代概念生成历史上合理的类比解释。与两位人文学者的定性诠释学探究揭示了真实性危机，两人都将约40%的真实维多利亚时代摘录误分类为机器生成。我们认为对未来的结构性无知将幻觉转化为对19世纪本体论的解释性探索。
英文摘要
Large Language Models (LLMs) are temporally overexposed: trained on vast contemporary corpora, they encode present-day concepts that make them unreliable narrators of the past. We present TimeCapsule, a 1.2B-parameter LLaMA-style causal model trained exclusively on Victorian texts (1800-1875) as an epistemologically isolated generative archive. Quantitative evaluation shows a 45.4% perplexity reduction over a GPT-2 baseline on held-out Victorian prose, while larger contemporary causal models achieve lower raw perplexity through broader pretraining but lack temporal isolation. TimeCapsule exhibits computational sensemaking, generating historically plausible analogical explanations for unfamiliar modern concepts (e.g., describing a computer as a "hypertrophied lung"). A qualitative hermeneutic probe with two humanities scholars revealed a crisis of authenticity, as both misclassified approximately 40% of genuine Victorian excerpts as machine-produced. We argue that structural ignorance of the future transforms hallucinations into interpretive probes of nineteenth-century ontologies.
发表机构
- Muhlenberg College（Muhlenberg学院）
机构由 AI 辅助整理，请以论文原文为准。
