The Conceptual Challenge of Evaluating Large Language Models: When Language Fails to Describe Novel Technology
By
cdrnsf
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Summary
The article examines the psychological and linguistic challenges in evaluating Large Language Models (LLMs), arguing that their novel nature defies existing conceptual frameworks. The author contends that LLMs are neither traditional machines nor minds, but something fundamentally different that lacks the conceptual capacity we attribute to thinking beings. This creates a vocabulary problem where we resort to anthropomorphic language (minds, thought) that misrepresents what LLMs actually are and shapes our understanding in misleading ways.
Key quotes
· 4 pulledLLMs are conceptually unlike anything we're used to dealing with in either a technical or social context; they are neither machines nor minds, but some third thing that is neither logical nor capable of conceptualizing.
And we've never really had to talk about something that, so, lacking a vocabulary to do, we tend to resort to a language of minds and of thought.
Those words shape our own understanding and evaluation of LLMs in ways that may be fundamentally misleading.
The lack of linguistic context creates significant problems in discussing LLMs, as our existing vocabulary fails to capture their true nature.
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