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thesephist.com | ||
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haifengl.wordpress.com
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| | | | | Generative artificial intelligence (GenAI), especially ChatGPT, captures everyone's attention. The transformerbased large language models (LLMs), trained on a vast quantity of unlabeled data at scale, demonstrate the ability to generalize to many different tasks. To understand why LLMs are so powerful, we will deep dive into how they work in this post. LLM Evolutionary Tree... | |
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douglasduhaime.com
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| | | | | Working notes & digital experiments | |
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transformer-circuits.pub
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| | | | | [AI summary] The text discusses the interpretability of features in a machine learning model, focusing on how features like Arabic, base64, and Hebrew are used in interpretable ways. It explores the extent to which these features explain the model's behavior, noting that features with higher activations are more interpretable. The text also addresses the limitations of current methods, such as the computational cost of simulating features and the potential for dataset correlations to influence feature interpretations. Finally, it concludes that the model's learning process creates a richer structure in its activations than the dataset alone, suggesting that feature-based interpretations provide meaningful insights into the model's behavior. | |
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christopher-beckham.github.io
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| | | Techniques for label conditioning in Gaussian denoising diffusion models | ||
