| tl:dr by GPT3 Paper contributes to debate about abilities of large language models like GPT-3 Evaluates how well GPT performs on the Turing Test Examines limits of such models, including tendency to generate falsehoods Considers social consequences of problems with truth-telling in these models Proposes formalization of "reversible questions" as a probabilistic measure Argues against claims that GPT-3 lacks semantic ability Offers theory on limits of large language models based on compression, priming, distributional semantics, and semantic webs Suggests that GPT and similar models prioritize plausibility over truth in order to maximize their objective function
Warns that widespread adoption of language generators as writing tools could result in permanent pollution of informational ecosystem with plausible but untrue texts. |