有生命还是无生命：这是大语言模型面临的问题
Animate, or Inanimate, That is the Question for Large Language Models
AI总结：
该研究针对大语言模型仅靠文本训练可能存在生命性理解局限的问题，通过多语境提示探测不同LLMs，发现其对典型生命/非生命实体的判断类人，且能识别异常事物的生命性，无需人类内隐认知机制。
摘要：
人类的认知本质与“生命性（animacy）”概念深度交织，该概念在塑造人类记忆、视觉和多层级语言理解方面发挥着核心作用。尽管生命性通过对动词、形容词的细微约束体现在语言中，但它也通过语言外信息被学习和完善。同理，我们认为大语言模型（LLMs）在处理生命性相关内容时自然语言理解能力有限，根源在于这些模型仅在文本数据上训练。\n因此，本文旨在回答的问题随之产生：凭借其数字化智能，LLMs能否以与人类相似的方式处理生命性？我们随后通过提示工程方法提出了一套系统性分析。具体而言，我们使用有生命、无生命、常见、陌生四类语境构造提示，对不同LLMs进行探测。结果显示，尽管LLMs主要在文本数据上训练，但在面对典型的有生命和无生命实体时，它们表现出与人类相似的行为，这与此前的研究结论一致。此外，LLMs能够通过将异常事物识别为有生命的来适应理解非常规情境，无需借助人类用于拆解生命性表征的内隐认知触发机制。
英文摘要：
The cognitive essence of humans is deeply intertwined with the concept of animacy, which plays an essential role in shaping their memory, vision, and multi-layered language understanding. Although animacy appears in language via nuanced constraints on verbs and adjectives, it is also learned and refined through extralinguistic information. Similarly, we assume that the LLMs' limited abilities to understand natural language when processing animacy are motivated by the fact that these models are trained exclusively on text. Hence, the question this paper aims to answer arises: can LLMs, in their digital wisdom, process animacy in a similar way to what humans would do? We then propose a systematic analysis via prompting approaches. In particular, we probe different LLMs by prompting them using animate, inanimate, usual, and stranger contexts. Results reveal that, although LLMs have been trained predominantly on textual data, they exhibit human-like behavior when faced with typical animate and inanimate entities in alignment with earlier studies. Hence, LLMs can adapt to understand unconventional situations by recognizing oddities as animated without needing to interface with unspoken cognitive triggers humans rely on to break down animations.
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