Realistic Evaluation of Model Merging for Compositional Generalization

from Arxiv Papers · host Igor Melnyk

This paper evaluates various model merging methods for compositional generalization in image classification, generation, and NLP, clarifying their merits, requirements, and computational costs in a shared experimental setting. https://arxiv.org/abs//2409.18314 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

Episode metadata supplied by the publisher feed · Published Oct 1, 2024

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Realistic Evaluation of Model Merging for Compositional Generalization

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