Innovating in the open w/ models of today’s capabilities makes responsibility more important than ever — we’re continuing to invest in responsible AI with a new Responsible Use Guide, continuous collaboration through community forums & red teaming exercises w/ third-parties.
@aiatmeta
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Llama Now Available Worldwide for Global Developers
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Llama is now available to people around the world and we can't wait to see what you build.
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AI Benefits for All: Llama 2 Access Democratizes Opportunities
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We believe that AI can and should benefit everyone. Increasing access to models like Llama 2 can help create a new era of economic and social opportunities for startups, entrepreneurs and researchers.
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Meta Releases Llama 2 Open Source Language Model Freely
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We believe an open approach is the right one for the development of today's Al models. Today, we’re releasing Llama 2, the next generation of Meta’s open source Large Language Model, available for free for research & commercial use. Details https://
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CM3leon: State-of-the-Art Multimodal Text-to-Image Generation Model
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Introducing CM3leon, a first-of-its-kind multimodal model that achieves state-of-the-art performance for text-to-image generation with 5x the compute efficiency of competitive models. More details https://
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Meta Open-Sources Speech Recognition Fairness Dataset
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In line with this work, we're open-sourcing a new dataset to help the broader community improve fairness of speech recognition models. The dataset includes ~27K utterances in recorded speech from 595 paid participants. Dataset https://
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Model Performance Improvements Across Demographics Without Fairness Tradeoffs
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Early experimental results show that our approach improves model performance on all demographic groups in our evaluation datasets, with the largest gains in respect to accent inclusivity — demonstrating that improved fairness doesn’t mean a performance tradeoff. 2/3
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Privacy-Preserving Approach Improves Speech Recognition Fairness
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Today we're announcing a new privacy-preserving approach to improve fairness & robustness of automatic speech recognition systems. This unique approach lets researchers improve ASR performance without relying on demographic data. More info
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Area Chair Award: Machine Translation with Minimal Data for Linguistic Diversity
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Area Chair Award on Linguistic Diversity
Small Data, Big Impact: Leveraging Minimal Data for Effective Machine Translation https://
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Language Model Acceptability Judgements Lack Contextual Robustness
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ACL Outstanding Paper
Language model acceptability judgements are not always robust to context