In large language models (LLMs) like ChatGPT, after the model processes the input, it produces a final hidden vector - a compact representation that summarizes the context up to that point. To convert this vector back into actual words, the model uses an unembedding matrix, effectively reversing the embedding step. This unembedding matrix is essentially the transpose of the original embedding matrix. It projects the hidden vector into the vocabulary space, producing a numerical score for every p

With the help of AI-driven large language models (LLMs), Nneka Mobisson, co-founder of mDoc in Nigeria, and her team are optimizing an end-to-end integrated healthcare experience that’s putting power in the hands of patients. Now, millions have the agency to lead happier, healthier lives. Step by step, innovators like Nneka Mobisson are using #AI to help save lives and accelerate progress toward a healthier world. #GrandChallenges https://gates.ly/3toKVj7 | Gates Foundation