Paper Reading AI Learner

Language Models Understand Us, Poorly

2022-10-19 15:58:59
Jared Moore
arXiv_AI
     
Abstract

Some claim language models understand us. Others won't hear it. To clarify, I investigate three views of human language understanding: as-mapping, as-reliability and as-representation. I argue that while behavioral reliability is necessary for understanding, internal representations are sufficient; they climb the right hill. I review state-of-the-art language and multi-modal models: they are pragmatically challenged by under-specification of form. I question the Scaling Paradigm: limits on resources may prohibit scaled-up models from approaching understanding. Last, I describe how as-representation advances a science of understanding. We need work which probes model internals, adds more of human language, and measures what models can learn.

Abstract (translated)

URL

https://arxiv.org/abs/2210.10684

PDF

https://arxiv.org/pdf/2210.10684.pdf

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