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Position Interpolation (PI) extends the context window sizes of pretrained LLMs with minimal fine-tuning, achieving strong results on tasks requiring long context while preserving quality on tasks within the original context window. PI linearly down-scales input position indices to avoid catastrophic attention scores.
https://arxiv.org/abs//2306.15595
YouTube: https://www.youtube.com/@ArxivPapers
PODCASTS:
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
By Igor Melnyk5
33 ratings
Position Interpolation (PI) extends the context window sizes of pretrained LLMs with minimal fine-tuning, achieving strong results on tasks requiring long context while preserving quality on tasks within the original context window. PI linearly down-scales input position indices to avoid catastrophic attention scores.
https://arxiv.org/abs//2306.15595
YouTube: https://www.youtube.com/@ArxivPapers
PODCASTS:
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

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