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Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions
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
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By Igor Melnyk
5
33 ratings
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
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