Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions

from Arxiv Papers · host Igor Melnyk

Insertion Language Models (ILMs) improve sequence generation by inserting tokens at arbitrary positions, outperforming autoregressive and masked diffusion models in planning tasks and offering flexibility in text infilling. https://arxiv.org/abs//2505.05755 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 May 12, 2025

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Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions

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