[2][3][page needed] The newer challenge arises from the inherent difficulty for users in distinguishing between genuine and misleading content produced by large language models (LLMs) as they become more adept at generating natural and contextually appropriate responses.
Foster-McBride demonstrated that newer LLMs, with improved architecture and training on extensive datasets, showed significant advancements across key performance metrics, including fluency and contextual understanding.
[5] This aspect of AI evolution posed a unique challenge: while the responses appeared more reliable, the underlying verisimilitude increased the potential for misinformation going unnoticed by human evaluators.
[5] The study concluded that as models became more capable, their fluency led to a rising trust among users, which paradoxically made discerning false information harder.
Similar concerns arise in Goodhart’s law, where an AI's optimization of specified objectives can lead to unintended, often negative, outcomes.