Diffusion Language Models (DLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. However, the potential of Retrieval-Augmented Generation (RAG), which shows great successes for enhancing large language models (LLMs), has not been well explored, due to the fundamental difference between LLM and DLM decoding. To fill this critical gap, we systematically test the performance of DLMs within the RAG framework. Our findings reveal that DLMs coupled with RAG show promising potentials with stronger dependency on contextual information, but suffer from limited generation precision. We identify a key underlying issue: Response Semantic Drift (RSD), where the generated answer progressively deviates from the query's original semantics, leading to low precision content. We trace this problem to the denoising strategies in DLMs, which fail to maintain semantic alignment with the query throughout the iterative denoising process. To address this, we propose Semantic-Preserving REtrieval-Augmented Diffusion (SPREAD), a novel framework that introduces a query-relevance-guided denoising strategy. By actively guiding the denoising trajectory, SPREAD ensures the generation remains anchored to the query's semantics and effectively suppresses drift. Experimental results demonstrate that SPREAD significantly enhances the precision and effectively mitigates RSD of generated answers within the RAG framework.

翻译：扩散语言模型（DLMs）近期在自然语言处理任务中展现出卓越能力。然而，检索增强生成（RAG）虽在增强大语言模型（LLMs）方面取得显著成功，但由于LLM与DLM解码机制的根本差异，其在DLMs中的应用潜力尚未得到充分探索。为填补这一关键空白，我们系统测试了RAG框架下DLMs的性能表现。研究发现：结合RAG的DLMs展现出依赖上下文信息的优势潜力，但存在生成精度受限的问题。我们揭示了其核心症结：响应语义漂移（RSD）现象——生成答案在迭代过程中逐渐偏离查询原始语义，导致内容精度降低。通过溯源分析，我们将该问题归因于DLMs的去噪策略未能贯穿迭代去噪过程保持与查询的语义对齐。为此，我们提出语义保持检索增强扩散框架（SPREAD），该创新框架引入查询相关性引导的去噪策略。通过主动引导去噪轨迹，SPREAD确保生成内容始终锚定查询语义，有效抑制语义漂移。实验结果表明，SPREAD在RAG框架内显著提升生成答案的精度，并有效缓解RSD现象。