The remarkable performance of large language models (LLMs) on complex
linguistic tasks has sparked a lively debate on the nature of their
capabilities. Unlike humans, these models learn language exclusively from
textual data, without direct interaction with the real world. Nevertheless,
they can generate seemingly meaningful text about a wide range of topics. This
impressive accomplishment has rekindled interest in the classical 'Symbol
Grounding Problem,' which questioned whether the internal representations and
outputs of classical symbolic AI systems could possess intrinsic meaning.
Unlike these systems, modern LLMs are artificial neural networks that compute
over vectors rather than symbols. However, an analogous problem arises for such
systems, which we dub the Vector Grounding Problem. This paper has two primary
objectives. First, we differentiate various ways in which internal
representations can be grounded in biological or artificial systems,
identifying five distinct notions discussed in the literature: referential,
sensorimotor, relational, communicative, and epistemic grounding.
Unfortunately, these notions of grounding are often conflated. We clarify the
differences between them, and argue that referential grounding is the one that
lies at the heart of the Vector Grounding Problem. Second, drawing on theories
of representational content in philosophy and cognitive science, we propose
that certain LLMs, particularly those fine-tuned with Reinforcement Learning
from Human Feedback (RLHF), possess the necessary features to overcome the
Vector Grounding Problem, as they stand in the requisite causal-historical
relations to the world that underpin intrinsic meaning. We also argue that,
perhaps unexpectedly, multimodality and embodiment are neither necessary nor
sufficient conditions for referential grounding in artificial systems.

通过探究内在表征的多种不同地基于生物或人工系统方法（指示性、感觉运动、关系、交流和认识性地基础），梳理分化它们之间的差异，并阐述说明认为指示性地基础是矢量基础问题的关键所在。基于哲学和认知科学中的表征内容理论，本文提出了某些大语言模型（LLMs，尤其是使用人类反馈从 RLHF 进行调整的）具有克服矢量基础问题所必需的特征，并且提出，多模态和体现都不是人工系统指示性基础的必要或充分条件。