大语言模型的输出其实没有真实意义，因它缺乏必要意图。
The Outputs of Large Language Models are Meaningless
- 认为语言输出需有意图才能有意义，而大模型无真实意图。
- 反驳外部语义论和内部语义论，指出其无法弥补意图缺失。
- 解释为何人们仍觉得模型输出有意义且可用作知识获取。
本文提出一个简单论证：大语言模型（LLMs）的输出本质上是无意义的。该论证基于两个关键前提：(a) 某些类型的意图是语言输出具有字面意义所必需的；(b) 大语言模型不可能具备此类意图。我们回应了多种可能反驳，包括语义外部主义主张——可由外部依赖替代意图，以及语义内部主义主张——意义可仅通过概念间的内在关系（如概念角色）定义。最后，我们讨论即便该论证成立，为何大模型输出仍显得有意义，且能帮助人们获得真信念甚至知识。
原文摘要 · Abstract (English)
In this paper, we offer a simple argument for the conclusion that the outputs of large language models (LLMs) are meaningless. Our argument is based on two key premises: (a) that certain kinds of intentions are needed in order for LLMs' outputs to have literal meanings, and (b) that LLMs cannot plausibly have the right kinds of intentions. We defend this argument from various types of responses, for example, the semantic externalist argument that deference can be assumed to take the place of intentions and the semantic internalist argument that meanings can be defined purely in terms of intrinsic relations between concepts, such as conceptual roles. We conclude the paper by discussing why, even if our argument is sound, the outputs of LLMs nevertheless seem meaningful and can be used to acquire true beliefs and even knowledge.
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