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2023-8 arXiv roundup: Look I gave a talk, SILO-ing language models, lots of MoE + tool use papers
(I’m still planning on doing weekly installments in general—I just got behind and it took a while to catch up). This newsletter made possible by…
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Davis Blalock
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2023-7-30 arXiv roundup: Better image captions, Scaling EMA, Chain of thought empiricism
This newsletter made possible by MosaicML. Also, heads up that we’ve hit the late summer slump in arXiv submissions, so there’s less content than usual…
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Davis Blalock
1
2023-7-23 arXiv roundup: OpenAI breaking changes, Much better attention and image captions
This newsletter made possible by MosaicML. Retentive Network: A Successor to Transformer for Large Language Models They introduce an exceptionally…
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Davis Blalock
7
2023-7-16 arXiv roundup: Weird step sizes help gradient descent, Better CPU matmuls
This newsletter made possible by MosaicML. Also a reminder that I’m now retweeting high-quality threads about papers to try to improve the…
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Davis Blalock
2023-7-9 arXiv roundup: LLMs ignore the middle of their context, MoE + instruction tuning rocks
This newsletter made possible by MosaicML. A mini-announcement Because it’s gotten increasingly difficult to find technical ML content in all the AI…
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Davis Blalock
2023-7-2 arXiv roundup: Self-supervised eval, Prompting text models like image models, KV cache eviction
This newsletter made possible by MosaicML. And thanks to @snowclipsed for the Twitter shoutout this week! Also, I wrote a blog post about using language…
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Davis Blalock
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Models generating training data: huge win or fake win?
Here’s a puzzle: We’ve seen a lot of papers claiming you can use one language model to generate useful training data for another language model. But…by…
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Davis Blalock
7
2023-6-25 arXiv roundup: Learning from textbooks, Eliminating transformer outliers, Zero++
This newsletter brought to you by MosaicML. Textbooks Are All You Need They got near-SotA code generation with a tiny 1.3B param model by curating an…
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Davis Blalock
Davis Summarizes Papers
Davis Summarizes Papers
I go through all the machine learning arXiv submissions each week and summarize 10 to 20 of my favorites. Free forever and read by thousands of ML researchers and practitioners.
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