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