Generative AI models, including Google's Gemini and OpenAI's GPT-4o, are found to hallucinate frequently, with reliability varying across different models. A study from Cornell and other institutions benchmarked these models against authoritative sources, revealing that even the best models only produce hallucination-free text about 35% of the time. The research highlights the need for caution in trusting AI outputs, as models often refuse to answer questions they might get wrong.
The study evaluated over a dozen popular AI models, including Meta's Llama and Anthropic's Claude, and found that models struggle more with questions outside of Wikipedia's scope. Despite claims from major AI companies about improvements, the results indicate that hallucination rates remain high. Researchers suggest that implementing human-in-the-loop fact-checking could help mitigate these issues, emphasizing the importance of developing advanced verification tools.
• Generative AI models frequently hallucinate, impacting their reliability.
• Study shows even top models only produce accurate outputs 35% of the time.
Hallucinations in AI can lead to significant trust issues regarding the outputs of generative models.
The study used benchmarking to assess how well different models performed on factual accuracy.
This method is suggested as a way to reduce hallucinations in generative AI models.
Google's Gemini model was evaluated in the study for its hallucination rates.
OpenAI's models, including GPT-4o, were central to the study's findings on hallucinations.
ABC (Australian Broadcasting Corporation) 7month
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