Deep Fusion Memory：一种面向大语言模型的块级参数化记忆架构
Deep Fusion Memory: A Chunk-Level Parametric Memory for Large Language Models
Deep Fusion Memory (DFM) 通过门控交叉注意力将预测的参数化记忆集成到中间隐藏状态，减少检索开销，在无需在线检索的情况下提升知识密集型任务表现。
Abstract
Deep Fusion Memory integrates predicted parametric memory into intermediate hidden states via gated cross-attention, reducing retrieval overhead and improving knowledge-intensive tasks without online retrieval.
Large language models need effective ways to acquire and use external knowledge beyond what they retain from pretraining. Continued pretraining risks catastrophic forgetting, while retrieval at inference time can add substantial overhead. External parametric memory avoids online retrieval, but constructing its training data requires extensive offline search, and output-level interpolation provides only shallow fusion. We introduce Deep Fusion Memory (DFM), a parametric memory architecture that reduces this construction workload and incorporates learned memory into intermediate hidden states. A causal memory predictor learns to generate memory from the preceding context, using retrieved chunk embeddings from the corpus as supervision. The predicted memory is shared across tokens within each chunk and fused into their hidden states through gated cross-attention. Memory prediction and fusion are trained end to end with the backbone frozen, and inference requires no explicit retrieval. Compared with existing parametric memory approaches, which often construct token-level memory targets, DFM requires 32times fewer retrieval queries and 2048times fewer retrieved items to construct its training data. Across six QA tasks, DFM performs better over the strongest baseline by 12.71% on Mistral-7B-v0.3 and 11.19% on Qwen3-8B-Base. Beyond QA, domain adaptation yields average gains of 6.51 and 4.38 points over Qwen3-4B-Base and Qwen3-8B-Base, respectively.
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来源: Hugging Face Daily Papers · huggingface.co