[LLM][Meaning][NLP] Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data

Mar 11, 2023

Another paper on large language models. This time, it directly addresses the question of “meaning”, and how systems purely based on “form” cannot attain meaning.

Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data

Emily M. Bender, Alexander Koller

Abstract

The success of the large neural language models on many NLP tasks is exciting. However, we find that these successes sometimes lead to hype in which these models are being described as “understanding” language or capturing “meaning”. In this position paper, we argue that a system trained only on form has a priori no way to learn meaning. In keeping with the ACL 2020 theme of “Taking Stock of Where We’ve Been and Where We’re Going”, we argue that a clear understanding of the distinction between form and meaning will help guide the field towards better science around natural language understanding.

https://aclanthology.org/2020.acl-main.463

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