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Focused Transformer: Contrastive Training for Context Scaling
The paper introduces the Focused Transformer (FoT), a technique that enhances the context length of large language models by addressing the distraction issue caused by overlapping keys. The method is demonstrated by fine-tuning OpenLLaMA checkpoints, resulting in models that excel in tasks requiring long context.
https://arxiv.org/abs//2307.03170
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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By Igor Melnyk
5
33 ratings
The paper introduces the Focused Transformer (FoT), a technique that enhances the context length of large language models by addressing the distraction issue caused by overlapping keys. The method is demonstrated by fine-tuning OpenLLaMA checkpoints, resulting in models that excel in tasks requiring long context.
https://arxiv.org/abs//2307.03170
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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