Can a machine understand the meanings of natural language? Recent
developments in the generative large language models (LLMs) of artificial
intelligence have led to the belief that traditional philosophical assumptions
about machine understanding of language need to be revised. This article
critically evaluates the prevailing tendency to regard machine language
performance as mere syntactic manipulation and the simulation of understanding,
which is only partial and very shallow, without sufficient referential
grounding in the world. The aim is to highlight the conditions crucial to
attributing natural language understanding to state-of-the-art LLMs, where it
can be legitimately argued that LLMs not only use syntax but also semantics,
their understanding not being simulated but duplicated; and determine how they
ground the meanings of linguistic expressions.

机器能否理解自然语言？该研究评估了人工智能生成型大型语言模型 (LLMs) 的最新发展，批评了把机器语言表现仅视为语法操作和模拟理解的传统哲学假设，并强调了将自然语言理解归因于最先进的 LLMs 的关键条件，认为 LLMs 不仅仅使用语法，还使用语义，理解不是模拟而是复制，同时确定了它们如何给予语言表达的意义基础。

大型语言模型中的意义和理解

Meaning and understanding in large language models

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。

向量基础问题

The Vector Grounding Problem

Most models tasked to ground referential utterances in 2D and 3D scenes learn
to select the referred object from a pool of object proposals provided by a
pre-trained detector. This is limiting because an utterance may refer to visual
entities at various levels of granularity, such as the chair, the leg of the
chair, or the tip of the front leg of the chair, which may be missed by the
detector. We propose a language grounding model that attends on the referential
utterance and on the object proposal pool computed from a pre-trained detector
to decode referenced objects with a detection head, without selecting them from
the pool. In this way, it is helped by powerful pre-trained object detectors
without being restricted by their misses. We call our model Bottom Up Top Down
DEtection TRansformers (BUTD-DETR) because it uses both language guidance (top
down) and objectness guidance (bottom-up) to ground referential utterances in
images and point clouds. Moreover, BUTD-DETR casts object detection as
referential grounding and uses object labels as language prompts to be grounded
in the visual scene, augmenting supervision for the referential grounding task
in this way. The proposed model sets a new state-of-the-art across popular 3D
language grounding benchmarks with significant performance gains over previous
3D approaches (12.6% on SR3D, 11.6% on NR3D and 6.3% on ScanRefer). When
applied in 2D images, it performs on par with the previous state of the art. We
ablate the design choices of our model and quantify their contribution to
performance. Our code and checkpoints can be found at the project website
this https URL

提出了一种语言引导的物体检测模型，使用底部和顶部的启发式信息来实现参考指称地面实体，同时获得了在流行的 3D 语言基础基准测试中的新的最先进的性能表现，与以前的技术相比显著提高。