The recent surge of generative AI has been fueled by the generative power of diffusion probabilistic models and the scalable capabilities of large language models. Despite their potential, it remains elusive whether diffusion language models can solve general language tasks comparable to their autoregressive counterparts. This paper demonstrates that scaling diffusion models w.r.t. data, sizes, and tasks can effectively make them strong language learners. We build competent diffusion language models at scale by first acquiring knowledge from massive data via masked language modeling pretraining thanks to their intrinsic connections. We then reprogram pretrained masked language models into diffusion language models via diffusive adaptation, wherein task-specific finetuning and instruction finetuning are explored to unlock their versatility in solving general language tasks. Experiments show that scaling diffusion language models consistently improves performance across downstream language tasks. We further discover that instruction finetuning can elicit zero-shot and few-shot in-context learning abilities that help tackle many unseen tasks by following natural language instructions, and show promise in advanced and challenging abilities such as reasoning

翻译：近期生成式人工智能的蓬勃发展得益于扩散概率模型的生成能力与大语言模型的可扩展性。尽管潜力巨大，但扩散语言模型能否像自回归模型一样解决通用语言任务仍不明确。本文证明：在数据、模型规模和任务维度上扩展扩散模型，可有效提升其语言学习能力。我们利用掩码语言建模预训练与扩散模型的内在关联，通过先验知识获取构建大规模高效扩散语言模型——首先通过掩码语言建模预训练从海量数据中获取知识，再通过扩散适配将预训练掩码语言模型重编程为扩散语言模型，并探索任务特定微调与指令微调以释放其解决通用语言任务的通用性。实验表明，扩展扩散语言模型可持续提升下游语言任务性能。进一步发现，指令微调能激发零样本与少样本情境学习能力，使模型通过遵循自然语言指令处理众多未见任务，并展现出在推理等高级挑战性任务中的应用潜力。