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[short] ReFT: Representation Finetuning for Language Models
Parameter-efficient fine-tuning methods are enhanced by Representation Finetuning (ReFT) techniques, particularly Low-rank Linear Subspace ReFT (LoReFT), which outperforms existing methods in efficiency and performance.
https://arxiv.org/abs//2404.03592
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
Parameter-efficient fine-tuning methods are enhanced by Representation Finetuning (ReFT) techniques, particularly Low-rank Linear Subspace ReFT (LoReFT), which outperforms existing methods in efficiency and performance.
https://arxiv.org/abs//2404.03592
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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