Large Language Models have demonstrated remarkable progress in general-purpose capabilities and can achieve strong performance in specific domains through fine-tuning on domain-specific data. However, acquiring high-quality data for target domains remains a significant challenge. Existing data synthesis approaches foll...
Tong Ye, Hang Yu, Tengfei Ma et al.· Proceedings of the 32nd ACM...· 1 citation
This work presents Consistency Forcing (CForce) for dLLMs, a distillation method to force the mask predictions of early stages to align with those of later stages, thereby improving training-inference alignment.
Yujie Ren, Chenkai Xu, Zhuocheng Gong et al.· 1 citation
This work proposes a novel framework, DOMINO, that learns a minimal sufficient domain representation from reference samples and leverages it to guide the generation of domain-aligned synthetic data, enabling practical and scalable domain adaptation without manual prompt design or natural language domain specifications.
Tong Ye, Hang Yu, Tengfei Ma et al.· Proceedings of the 32nd ACM...· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
