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I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data?
The paper presents a generative model demonstrating that large language models learn human-interpretable concepts, supporting the linear representation hypothesis through theoretical and empirical evaluations.
https://arxiv.org/abs//2503.08980
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
The paper presents a generative model demonstrating that large language models learn human-interpretable concepts, supporting the linear representation hypothesis through theoretical and empirical evaluations.
https://arxiv.org/abs//2503.08980
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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