CMT: A Memory Compression Method for Continual Knowledge Learning of Large Language Models

• URL: http://arxiv.org/abs/2412.07393v1
• Date: Tue, 10 Dec 2024 10:35:19 GMT
• Title: CMT: A Memory Compression Method for Continual Knowledge Learning of Large Language Models
• Authors: Dongfang Li, Zetian Sun, Xinshuo Hu, Baotian Hu, Min Zhang,
• Abstract summary: Large Language Models (LLMs) need to adapt to the continuous changes in data, tasks, and user preferences. To address these challenges, this paper proposes the Compression Memory Training (CMT) method. CMT compresses and extracts information from new documents to be stored in a memory bank. When answering to queries related to these new documents, the model aggregates these document memories from the memory bank to better answer user questions.
• Score: 22.93893181000535
• License:
• Abstract: Large Language Models (LLMs) need to adapt to the continuous changes in data, tasks, and user preferences. Due to their massive size and the high costs associated with training, LLMs are not suitable for frequent retraining. However, updates are necessary to keep them in sync with rapidly evolving human knowledge. To address these challenges, this paper proposes the Compression Memory Training (CMT) method, an efficient and effective online adaptation framework for LLMs that features robust knowledge retention capabilities. Inspired by human memory mechanisms, CMT compresses and extracts information from new documents to be stored in a memory bank. When answering to queries related to these new documents, the model aggregates these document memories from the memory bank to better answer user questions. The parameters of the LLM itself do not change during training and inference, reducing the risk of catastrophic forgetting. To enhance the encoding, retrieval, and aggregation of memory, we further propose three new general and flexible techniques, including memory-aware objective, self-matching and top-aggregation. Extensive experiments conducted on three continual learning datasets (i.e., StreamingQA, SQuAD and ArchivalQA) demonstrate that the proposed method improves model adaptability and robustness across multiple base LLMs (e.g., +4.07 EM & +4.19 F1 in StreamingQA with Llama-2-7b).

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