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generative AI modelScienceDaily
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80% Informative
Large language models can achieve incredible performance on some tasks without having internalized a coherent model of the world or the rules that govern it.
This means these models are likely to fail unexpectedly if they are deployed in situations where the environment or task slightly changes.
MIT researchers found that a popular type of generative AI model can provide turn-by-turn driving directions in New York City with near-perfect accuracy.
The researchers demonstrated the implications of this by adding detours to the map of New York City , which caused all the navigation models to fail.
If scientists want to build LLMs that can capture accurate world models, they need to take a different approach.
The researchers want to tackle a more diverse set of problems such as those where some rules are only partially known.
VR Score
91
Informative language
96
Neutral language
71
Article tone
semi-formal
Language
English
Language complexity
61
Offensive language
not offensive
Hate speech
not hateful
Attention-grabbing headline
not detected
Known propaganda techniques
not detected
Time-value
long-living
External references
no external sources
Source diversity
no sources
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