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Mitigating stereotypical biases in text to image generative systems
In this work, we propose a method to mitigate such biases and ensure that the outcomes are fair across different groups of people.
Cabrita: closing the gap for foreign languages
The main solution to overcome the cost challenge is to rely on available pre-trained models, which, despite recent advancements such as the LLaMA and LLaMA-2 models, still demonstrate inefficiency for certain specific domain problems or prove ineffective in scenarios involving conversational memory resources, given the large number of tokens required to represent text.
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