With the advent of large language models (LLMs), the trend in NLP has been to
train LLMs on vast amounts of data to solve diverse language understanding and
generation tasks. The list of LLM successes is long and varied. Nevertheless,
several recent papers provide empirical evidence that LLMs fail to capture
important aspects of linguistic meaning. Focusing on universal quantification,
we provide a theoretical foundation for these empirical findings by proving
that LLMs cannot learn certain fundamental semantic properties including
semantic entailment and consistency as they are defined in formal semantics.
More generally, we show that LLMs are unable to learn concepts beyond the first
level of the Borel Hierarchy, which imposes severe limits on the ability of
LMs, both large and small, to capture many aspects of linguistic meaning. This
means that LLMs will continue to operate without formal guarantees on tasks
that require entailments and deep linguistic understanding.

本文研究大语言模型在自然语言处理中的局限性，特别是无法学习一些基本语义属性，如语义蕴涵和一致性，以及不能学习超越 Borel 层次结构的概念，这对语言模型的语言理解能力产生了严重限制。

使用语言模型的学习界限

Limits for Learning with Language Models

Recursive neural network models and their accompanying vector representations
for words have seen success in an array of increasingly semantically
sophisticated tasks, but almost nothing is known about their ability to
accurately capture the aspects of linguistic meaning that are necessary for
interpretation or reasoning. To evaluate this, I train a recursive model on a
new corpus of constructed examples of logical reasoning in short sentences,
like the inference of "some animal walks" from "some dog walks" or "some cat
walks," given that dogs and cats are animals. This model learns representations
that generalize well to new types of reasoning pattern in all but a few cases,
a result which is promising for the ability of learned representation models to
capture logical reasoning.

该研究使用构造的逻辑推理短句的语料库训练了一种递归模型，其使用的递归神经网络模型和相应的词向量表示已在各种越来越语义复杂的任务中实现成功，但几乎不知道它们是否能准确捕捉必要的语言意义方面。该模型学习到的表示形式在所有但少数情况下都很好地推广到新的推理模式，这一结果为掌握逻辑推理和学习表示模型的能力提供了希望。