As Lexin Zhou points out, the performance of language models is a mixed bag: they show improvements in handling complex tasks, yet their reliability in basic queries diminishes, raising concerns about their practical utility in diverse job settings.
Jose Hernandez-Orallo highlights a troubling duality: these models excel at complex challenges but stumble on simple tasks, leading to potential misinformation and reliability issues that complicate their integration into roles requiring accuracy.
Despite advancements, Zhou notes that the situation appears to be deteriorating. Models like ChatGPT-4 may take excessively long to attempt answers rather than simply admitting lack of knowledge, leading to inefficiency.
The difference between human expectations and the models' performance is exacerbating the reliability issue, as users progressively pose harder questions, inadvertently setting the stage for greater discrepancies in perceived capability.
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