NGM: A Plug-and-Play Training-Free Memory Module for LLMs
The paper introduces N-gram Memory (NGM), a training-free memory module designed for large language models (LLMs). NGM utilizes a Causal N-Gram Encoder and a Cosine-Gated Memory Injector to enhance knowledge retrieval without the need for additional training. Evaluation results show that NGM improves performance across various benchmarks, particularly in code generation and knowledge-intensive tasks.
- ▪NGM is a plug-and-play memory module that does not require training.
- ▪It combines a Causal N-Gram Encoder with a Cosine-Gated Memory Injector for efficient knowledge retrieval.
- ▪The module shows performance improvements of 0.5 to 1.2 points on average across eight benchmarks.
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Computer Science > Artificial Intelligence arXiv:2605.16893 (cs) [Submitted on 16 May 2026] Title:NGM: A Plug-and-Play Training-Free Memory Module for LLMs Authors:Yuwen Qu, Wenhui Dong, Chenyang Si, Caifeng Shan View a PDF of the paper titled NGM: A Plug-and-Play Training-Free Memory Module for LLMs, by Yuwen Qu and 3 other authors View PDF HTML (experimental) Abstract:Recent studies introduce conditional memory modules that decouple knowledge storage from neural computation, enabling more direct knowledge access. Compared to MoE, which relies on dynamic computation paths, explicit lookup provides a more efficient knowledge retrieval mechanism. However, these approaches still depend on learned memory embeddings, requiring additional training and limiting flexibility.
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