A Continuous-Time Markov Chain Framework for Insertion Language Models
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Computer Science > Machine Learning
Title:A Continuous-Time Markov Chain Framework for Insertion Language Models
Abstract:Insertion Language Models (ILMs) offer several advantages over left-to-right generation and mask-based generation. However, existing formulations of insertion-based generation have largely been ad-hoc. In this paper, we derive a diffusion-style denoising objective for ILMs from first principles by formulating the noising process as a continuous-time Markov chain on the space of variable-length sequences. We show that previous formulations of ILMs can be viewed as special cases of this denoising framework. Through empirical evaluation on a synthetic planning task, we show that the proposed approach retains the benefits of insertion-based generation over left-to-right generation and masked diffusion models. In language modeling, our diffusion-based approach is competitive with left-to-right generation and masked diffusion models, while offering additional flexibility in sampling compared to existing insertion language models.
| Comments: | Accepted at AISTATS 2026. Code is available at this https URL |
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.10199 [cs.LG] |
| (or arXiv:2606.10199v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.10199
arXiv-issued DOI via DataCite (pending registration)
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