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Recent years have seen a surge in the popularity of commercial AI products based on generative, multi-purpose AI systems promising a unified approach to building machine learning (ML) models into technology.
Language models are few-shot learners
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A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model. In Proceedings of BigScience Episode #5 – Workshop on Challenges & Perspectives in Creating Large Language Models . Association for Computational Linguistics, virtual+Dublin, 84–94
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
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The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
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Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurement
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DiffusionDB: A Large-Scale Prompt Gallery Dataset for Text-to-Image Generative Models
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BLOOM: A 176B-parameter open-access multilingual language model
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Confirmed: the new Bing runs on OpenAI’s GPT-4
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Bard can now connect to your Google apps and services
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AI chatbots lose money every time you use them. That is a problem
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Stable Diffusion Prompts
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ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation
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Reducing the Carbon Impact of Generative AI Inference (today and in 2035). In Proceedings of the 2nd Workshop on Sustainable Computer Systems . 1–7
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