Language Models Understand Us, Poorly

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 (§2). I argue that while behavioral reliability is necessary for understanding, internal representations are sufficient; they climb the right hill (§3). I review state-of-the-art language and multi-modal models: they are pragmatically challenged by underspecification of form (§4). I question the Scaling Paradigm: limits on resources may prohibit scaled-up models from approaching understanding (§5). 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 (§6).