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Researchers from MIT and the MIT-IBM Watson AI Lab have developed a technique to estimate the reliability of foundation models, which are massive deep-learning models pretrained on a large amount of unlabeled data. These models, such as ChatGPT and DALL-E, can sometimes provide incorrect or misleading information, leading to potentially serious consequences. The researchers trained a set of slightly different foundation models and used an algorithm to assess the consistency of the representations each model learned about the same test data point.
By comparing their technique with baseline methods, they found that it better captured the reliability of foundation models across various classification tasks. This technique could be useful in determining whether a model should be applied in a specific setting, even without testing it on a real-world dataset, which can be particularly beneficial in privacy-sensitive environments like healthcare. The researchers also mentioned the possibility of ranking models based on reliability scores, allowing users to choose the most suitable one for their task.
Brief news summary
Researchers from MIT and the MIT-IBM Watson AI Lab have developed a technique to assess the reliability of foundation models, large pretrained deep-learning models used in artificial intelligence (AI) applications. The technique involves training multiple models that are slightly different from one another and using an algorithm to evaluate the consistency of their representations. If the representations are consistent, it indicates that the model is reliable. The approach was found to be more effective than baseline methods in capturing the reliability of foundation models across various classification tasks. This technique could be used to determine whether a model should be deployed in a specific setting, without the need for real-world testing. It could also be used to rank models based on reliability scores, allowing users to select the most suitable model for their task. The research paper will be presented at the Conference on Uncertainty in Artificial Intelligence.
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