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[QA] Density estimation with LLMs: a geometric investigation of in-context learning trajectories
This study explores LLaMA-2's in-context learning for probability density estimation, revealing unique learning trajectories and interpreting its behavior as adaptive kernel density estimation.
https://arxiv.org/abs//2410.05218
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 study explores LLaMA-2's in-context learning for probability density estimation, revealing unique learning trajectories and interpreting its behavior as adaptive kernel density estimation.
https://arxiv.org/abs//2410.05218
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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