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Learning without training: The implicit dynamics of in-context learning
This paper explores how stacking a self-attention layer with an MLP in transformers enables Large Language Models to learn in context by implicitly modifying MLP weights based on presented examples.
https://arxiv.org/abs//2507.16003
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
This paper explores how stacking a self-attention layer with an MLP in transformers enables Large Language Models to learn in context by implicitly modifying MLP weights based on presented examples.
https://arxiv.org/abs//2507.16003
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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