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Reliable AI: grounding to mitigate hallucinations

Published by Primer

This whitepaper addresses the challenge of AI hallucinations in generative models, where AI produces plausible yet incorrect information due to its training method. It highlights the risks such hallucinations pose, especially in critical decision-making environments like national security. To mitigate these risks, the paper introduces grounding techniques, which ensure that AI responses are based on verified and trusted data sources rather than predictions. By integrating information retrieval and grounding, AI systems can generate more reliable and accurate outputs, improving trust and utility in real-world applications.

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Related Categories Artificial Intelligence, Deep Learning, AI Ethics, AI Platforms, AI Applications, AI Chips, AI Processing Units, Unsupervised Learning, Reinforcement Learning, ML Algorithms, Data Preprocessing, Model Training, Model Evaluation

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