A study by Giskard highlights that instructing AI chatbots to provide concise answers can lead to increased hallucinations, particularly with ambiguous topics. Researchers found that short-answer prompts adversely affect models' factuality, revealing that leading AI models like GPT-4o and Claude 3.7 suffer from this issue. The study suggests that brevity compromises the ability to clarify misconceptions, as models may lack the necessary detail to debunk misinformation. This finding is particularly significant for AI deployment, where concise outputs are often prioritized for efficiency.
Our data shows that simple changes to system instructions dramatically influence a model's tendency to hallucinate.
When forced to keep it short, models consistently choose brevity over accuracy.
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