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Answer :
Generative AI models struggle with consistency due to unpredictability in their outputs and challenges in aligning training feedback with performance outcomes.
One of the key challenges faced by generative AI models in terms of consistency is their tendency to produce results that can be unpredictable and difficult to control. This inconsistency arises partly from the complexity of interpreting training feedback, which may not always align with desired performance outcomes. According to Maity (2019), AI systems often struggle to interpret training feedback and link it effectively to performance, training transfer, and trainee characteristics, making it hard to standardize processes and interpret results uniformly across different scenarios. Furthermore, as generative AI systems like ChatGPT and Bard become more advanced, their ability to generate convincing yet false statements introduces a risk of misinformation, complicating efforts to maintain accuracy and reliability in AI-generated content.
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