Natural Language Processing prides itself to be an empirically-minded, if not
outright empiricist field, and yet lately it seems to get itself into
essentialist debates on issues of meaning and measurement ("Do Large Language
Models Understand Language, And If So, How Much?"). This is not by accident:
Here, as everywhere, the evidence underspecifies the understanding. As a
remedy, this paper sketches the outlines of a model of understanding, which can
ground questions of the adequacy of current methods of measurement of model
quality. The paper makes three claims: A) That different language use situation
types have different characteristics, B) That language understanding is a
multifaceted phenomenon, bringing together individualistic and social
processes, and C) That the choice of Understanding Indicator marks the limits
of benchmarking, and the beginnings of considerations of the ethics of NLP use.

自然语言处理领域对于意义和测量的问题进行了实证研究，本文勾勒了一种理解模型，用于评估当前模型质量测量方法的适应性，并指出不同语言使用情境类型的特点、语言理解是多方面现象，以及理解指标的选择标志了对基准测试和语言处理伦理的考虑的开端。