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I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm
Manage episode 434665596 series 3524393
The paper introduces I-SHEEP, a continuous self-alignment paradigm for LLMs, significantly improving performance on various benchmarks compared to traditional one-time alignment methods.
https://arxiv.org/abs//2408.08072
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
--- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support
1493 episodes
Manage episode 434665596 series 3524393
The paper introduces I-SHEEP, a continuous self-alignment paradigm for LLMs, significantly improving performance on various benchmarks compared to traditional one-time alignment methods.
https://arxiv.org/abs//2408.08072
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
--- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support
1493 episodes
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