arXiv:2502.20469cs.CL2025-02被引 8

人类能区分'不可能'与'无法想象',且语言模型可辅助识别这种差异。

Shades of Zero: Distinguishing Impossibility from Inconceivability

  • 通过分类实验发现人能明确区分'不可能'与'无法想象'的事件
  • 主观概率评分无法区分两者,均接近零
  • 语言模型的字符串概率可预测人类对事件可能性的判断

某些事情是不可能的,但有些事情甚至比不可能更不可想象。在我们的世界中,仅凭意念悬浮羽毛是不可能的,但它符合我们对可能世界的直觉理论;而用数字五悬浮羽毛则在任何可能世界都无法设想('不可想象')。尽管已有研究探讨了不可能与极不可能事件的区别,但关于不可想象性的实证研究仍很匮乏。本文通过类比于探究不可能与极不可能差异的方法,考察人们是否能区分不可能与不可想象,并探讨其认知机制。实验1显示,人们能轻易区分这两类事件;实验2表明,主观概率评分在两类事件间无差异,均趋近于零;实验3检验统计语言模型对事件描述的概率估计是否能区分这两种模态,并与人类判断是否一致。结果发现,人与语言模型在区分上具高度相似性:两者均能有效区分不可能与不可想象事件,且模型生成的概率可预测人类对事件可能性的判断。研究提示,对极度罕见事件(如不可能与不可想象)的精细知识可能通过语言形式的统计学习获得,但仍存疑问:人类是否将不可能与不可想象视为类别差异而非程度差异。

原文摘要 · Abstract (English)

Some things are impossible, but some things may be even more impossible than impossible. Levitating a feather using one's mind is impossible in our world, but fits into our intuitive theories of possible worlds, whereas levitating a feather using the number five cannot be conceived in any possible world ("inconceivable"). While prior work has examined the distinction between improbable and impossible events, there has been little empirical research on inconceivability. Here, we investigate whether people maintain a distinction between impossibility and inconceivability, and how such distinctions might be made. We find that people can readily distinguish the impossible from the inconceivable, using categorization studies similar to those used to investigate the differences between impossible and improbable (Experiment 1). However, this distinction is not explained by people's subjective ratings of event likelihood, which are near zero and indistinguishable between impossible and inconceivable event descriptions (Experiment 2). Finally, we ask whether the probabilities assigned to event descriptions by statistical language models (LMs) can be used to separate modal categories, and whether these probabilities align with people's ratings (Experiment 3). We find high-level similarities between people and LMs: both distinguish among impossible and inconceivable event descriptions, and LM-derived string probabilities predict people's ratings of event likelihood across modal categories. Our findings suggest that fine-grained knowledge about exceedingly rare events (i.e., the impossible and inconceivable) may be learned via statistical learning over linguistic forms, yet leave open the question of whether people represent the distinction between impossible and inconceivable as a difference not of degree, but of kind.

认知科学语言模型不可能性

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