让扩散模型动态调整外部知识引用强度，提升问答准确性
Adaptive Guidance for Retrieval-Augmented Masked Diffusion Models
- 根据检索内容与模型知识的差异程度，动态调节引导强度
- 在多个问答数据集上优于现有检索增强方法，最高提升6.2%准确率
- 无需训练即可适配不同场景，适合知识密集型生成任务
检索增强生成（RAG）通过引入外部知识提升语言模型的事实准确性。然而，当检索到的上下文存在噪声、不可靠或与模型参数知识不一致时，会引发检索优先冲突，降低生成质量。尽管该问题在自回归语言模型中已有研究，但在基于扩散的语言模型中仍鲜有探索——其迭代去噪过程为整合检索内容带来了独特挑战。本文提出一种无需训练的自适应引导框架：自适应检索增强掩码扩散模型（ARAM），用于掩码扩散模型（MDMs）在RAG设置下的应用。ARAM根据检索内容引起的分布偏移信号噪声比（SNR），动态校准去噪过程中的引导强度。直观而言，当检索内容提供可靠修正证据时加强引导，而当上下文信号嘈杂或无支持时则抑制引导。在多个知识密集型问答基准上的大量实验表明，ARAM在整体问答性能上超越了多种竞争性RAG基线。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) improves factual grounding by incorporating external knowledge into language model generation. However, when retrieved context is noisy, unreliable, or inconsistent with the model's parametric knowledge, it introduces retrieval-prior conflicts that can degrade generation quality. While this problem has been studied in autoregressive language models, it remains largely unexplored in diffusion-based language models, where the iterative denoising process introduces unique challenges for integrating retrieved context. In this work, we propose Adaptive Retrieval-Augmented Masked Diffusion (ARAM), a training-free adaptive guidance framework for Masked Diffusion Models (MDMs) in RAG settings. ARAM dynamically calibrates the guidance scale during denoising according to the Signal-to-Noise Ratio (SNR) of the distributional shift induced by retrieved context. Intuitively, the model strengthens guidance when the retrieved context provides reliable corrective evidence and suppresses it when the contextual signal is noisy or non-supportive. Extensive experiments on multiple knowledge-intensive QA benchmarks show that ARAM improves overall QA performance over competitive RAG baselines.
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