3 papers
cs.CL2025
DLM-One: Diffusion Language Models for One-Step Sequence Generation
Tianqi Chen, Shujian Zhang, Mingyuan Zhou
This paper introduces DLM-One, a score-distillation-based framework for one-step sequence generation with continuous diffusion language models (DLMs). DLM-One eliminates the need f…
cs.CV2025
Score Forgetting Distillation: A Swift, Data-Free Method for Machine Unlearning in Diffusion Models
Tianqi Chen, Shujian Zhang, Mingyuan Zhou
The machine learning community is increasingly recognizing the importance of fostering trust and safety in modern generative AI (GenAI) models. We posit machine unlearning (MU) as…
cs.CL2025
Preference-grounded Token-level Guidance for Language Model Fine-tuning
Shentao Yang, Shujian Zhang, Congying Xia +3
Aligning language models (LMs) with preferences is an important problem in natural language generation. A key challenge is that preferences are typically provided at the sequence l…