MEMORYLLM: Towards Self-Updatable Large Language Models (2402.04624v2)

Abstract: Existing LLMs usually remain static after deployment, which might make it hard to inject new knowledge into the model. We aim to build models containing a considerable portion of self-updatable parameters, enabling the model to integrate new knowledge effectively and efficiently. To this end, we introduce MEMORYLLM, a model that comprises a transformer and a fixed-size memory pool within the latent space of the transformer. MEMORYLLM can self-update with text knowledge and memorize the knowledge injected earlier. Our evaluations demonstrate the ability of MEMORYLLM to effectively incorporate new knowledge, as evidenced by its performance on model editing benchmarks. Meanwhile, the model exhibits long-term information retention capacity, which is validated through our custom-designed evaluations and long-context benchmarks. MEMORYLLM also shows operational integrity without any sign of performance degradation even after nearly a million memory updates. Our code and model are open-sourced at https://github.com/wangyu-ustc/MemoryLLM.

MemoryLLM: Towards Self-Updatable LLMs

The paper "MemoryLLM: Towards Self-Updatable LLMs" explores the development of LLMs that can integrate new knowledge post-deployment, focusing on the challenge of maintaining an up-to-date repository of knowledge without degrading existing learned capabilities. Standard LLMs face limitations in knowledge updating, primarily remaining static after deployment, thereby rendering them less effective in capturing new data and evolving contexts. This work aims to address these problems by introducing MemoryLLM, a novel model architecture embedding a fixed-size memory pool within the latent space of the LLM, offering a mechanism for self-updating capabilities.

Key Contributions

1. Integrated Memory Pool Design: The paper introduces a fixed-size memory pool as part of the LLM's latent space, where the memory pool acts as a self-updatable parameter. This architecture allows the model to retain new information efficiently while phasing out outdated and redundant data.
2. Self-Update Mechanism: MemoryLLM features a self-updating functionality which permits the model to absorb new knowledge selectively at each layer of its architecture. This process, inspired by human cognitive memory circuits, allows the model to have a dynamic balance between integrating new data and sustaining pertinent old information. The update mechanism ensures that MemoryLLM retains operational integrity without performance regression even after numerous updates.
3. Scalable Knowledge Integration: The model's design maintains a consistent memory size, circumventing the issue of uncontrollable growth typical in retrieval-based architectures. Each self-update efficiently incorporates new knowledge through a cautious but systematic memory token refresh process across each transformer layer, which acts multiplicatively in retaining long-term data.

Evaluation

The paper meticulously evaluates MemoryLLM across several benchmarks to validate its capabilities and advantages, specifically focusing on the following:

1. Knowledge Integration: Evaluations using model editing benchmarks show that MemoryLLM allows for the successful integration of new knowledge. The LLM manages to score high on various question-answering tasks and model editing scenarios, indicating an improvement over existing methods in maintaining updated and accurate information processing.
2. Long-Term Knowledge Retention: Experimentation on long-context benchmarks demonstrates MemoryLLM's ability to recall and utilize information effectively across extended periods. Custom retention experiments indicate how well the LLM maintains previously stored knowledge even after repeated updates.
3. Robustness: MemoryLLM sustains operational capability and performance through nearly a million memory updates, underscoring its design effectiveness in preventing knowledge degradation over numerous iterations.

Implications and Future Directions

The advancement described in MemoryLLM holds significant implications for future AI developments and operational deployment. Practically, it bestows LLMs with capabilities akin to continuous learning, presenting strategies to actively evolve with the inflow of new information without needing complete retraining. Theoretically, it provokes further exploration into memory management techniques within transformer networks, potentially reducing the overhead of data redundancy and processing constraints.

The authors suggest that enhancing memory size and compression ratio could further bolster knowledge retention abilities, paving the way for models extending beyond current benchmarks. Extending MemoryLLM to multi-modal settings may adapt the constructs to varied domains, including scenarios with high-dimensional input like video and audio, simulating complex multimodal interactions dynamically.

This refined approach to LLM development elucidates an efficient balance between knowledge integration and memorization constraints, setting a foundation for scalable, adaptive AI systems.