Large language models and their big bullshit potential

Fisher, Sarah A. 2024. Large language models and their big bullshit potential. Ethics and Information Technology 26 , 67. 10.1007/s10676-024-09802-5

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (789kB) | Preview

Abstract

Newly powerful large language models have burst onto the scene, with applications across a wide range of functions. We can now expect to encounter their outputs at rapidly increasing volumes and frequencies. Some commentators claim that large language models are bullshitting, generating convincing output without regard for the truth. If correct, that would make large language models distinctively dangerous discourse participants. Bullshitters not only undermine the norm of truthfulness (by saying false things) but the normative status of truth itself (by treating it as entirely irrelevant). So, do large language models really bullshit? I argue that they can, in the sense of issuing propositional content in response to fact-seeking prompts, without having first assessed that content for truth or falsity. However, I further argue that they need not bullshit, given appropriate guardrails. So, just as with human speakers, the propensity for a large language model to bullshit depends on its own particular make-up.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	English, Communication and Philosophy
Publisher:	Springer
ISSN:	1572-8439
Date of First Compliant Deposit:	17 September 2024
Date of Acceptance:	17 September 2024
Last Modified:	07 Oct 2024 09:14
URI:	https://orca.cardiff.ac.uk/id/eprint/172197

Actions (repository staff only)

Edit Item