The Human and the Mechanical: logos, truthfulness, and ChatGPT

TL;DR

This paper argues that ChatGPT and similar models cannot genuinely form beliefs or veridical judgments, as they lack the necessary evidence-based and subjective components that humans use to assess truthfulness.

Contribution

It provides a semantic argument highlighting the fundamental differences between human and mechanical minds in forming truthful assertions.

Findings

Mechanical models lack evidence relating to reality

They do not possess endogenous evidence or beliefs

Models can only mimic veridical judgments, not genuinely form them

Abstract

The paper addresses the question of whether it is appropriate to talk about `mechanical minds' at all, and whether ChatGPT models can indeed be thought of as realizations of that. Our paper adds a semantic argument to the current debate. The act of human assertion requires the formation of a veridicality judgment. Modification of assertions with modals (John must be at home) and the use of subjective elements (John is obviously at home) indicate that the speaker is manipulating her judgments and, in a cooperative context, intends her epistemic state to be transparent to the addressee. Veridicality judgments are formed on the basis of two components: (i) evidence that relates to reality (exogenous evidence) and (ii) endogenous evidence, such as preferences and private beliefs. `Mechanical minds' lack these two components: (i) they do not relate to reality and (ii) do not have endogenous evidence. Therefore they lack the ability to form a belief about the world and a veridicality judgments altogether. They can only mimic that judgment, but the output is not ground in the very foundations for it.