Larimar: Large Language Models with Episodic Memory Control
(2403.11901)Abstract
Efficient and accurate updating of knowledge stored in LLMs is one of the most pressing research challenges today. This paper presents Larimar - a novel, brain-inspired architecture for enhancing LLMs with a distributed episodic memory. Larimar's memory allows for dynamic, one-shot updates of knowledge without the need for computationally expensive re-training or fine-tuning. Experimental results on multiple fact editing benchmarks demonstrate that Larimar attains accuracy comparable to most competitive baselines, even in the challenging sequential editing setup, but also excels in speed - yielding speed-ups of 4-10x depending on the base LLM - as well as flexibility due to the proposed architecture being simple, LLM-agnostic, and hence general. We further provide mechanisms for selective fact forgetting and input context length generalization with Larimar and show their effectiveness.
Overview
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The paper introduces Larimar, a novel architecture integrating an episodic memory with LLMs to allow for dynamic knowledge updates.
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Larimar's architecture features an encoder, a distributed associative memory, and a decoder, supporting efficient one-shot learning for rapid updating without re-training.
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Experimental results show that Larimar significantly speeds up knowledge editing tasks while maintaining accuracy, demonstrating scalability and flexibility in handling evolving information.
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The development suggests a future for AI where models can adapt to new information swiftly with an enhanced understanding of context, leading towards AI systems capable of human-like reasoning.
Enhancing LLMs with Episodic Memory for Dynamic Knowledge Updates
Introduction
In the rapidly evolving landscape of artificial intelligence research, the augmentation of LLMs with external memory models proposes a promising avenue for addressing the challenges of knowledge dynamism. The paper introduces Larimar, a novel architecture that embeds a distributed episodic memory within LLMs to facilitate on-the-fly knowledge updating. This breakthrough architecture promises significant improvements in speed, flexibility, and scalability over existing models.
Model Architecture
Larimar is premised on the understanding that traditional LLMs, while powerful, are constrained by static knowledge bases that are not readily updatable without extensive re-training. The proposed architecture counters this limitation by integrating an episodic memory modeled on the human hippocampus, which is renowned for its rapid learning capabilities. Larimar's architecture features:
- An encoder for converting data inputs into latent vectors.
- A distributed associative memory for storing and dynamically updating these vectors.
- A decoder that leverages both the static knowledge embedded in the LLM parameters and the dynamic updates stored in the memory.
The architecture supports efficient one-shot learning, enabling immediate memory updates without gradient descent, thus accelerating the updating process significantly.
Memory Operations
The memory model supports basic operations akin to write, read, and generate, enabling dynamic updates, retrieval, and use of stored knowledge to influence model outputs. Furthermore, sequential writing and forgetting operations are detailed, showcasing Larimar's ability to modify its memory contents accurately in response to evolving information needs.
Experimental Results
Empirical evaluations demonstrate Larimar's capability to perform knowledge editing tasks with speed-ups ranging from 4-10x compared to leading baselines while maintaining competitive accuracy. The architecture's flexibility is further evidenced through applications in sequential fact editing and selective fact forgetting. Notably, Larimar exhibits resilience in retaining its performance even as memory demands scale, showcasing its potential for practical, real-world applications where knowledge bases are continually updated.
Speculations on Future Developments in AI
The introduction of episodic memory into LLMs as explored by Larimar opens up exciting prospects for the future of AI. It is conceivable that as techniques for dynamic memory management and integration with LLMs evolve, we could witness the emergence of models that not only adapt to new information more swiftly but do so with an enhanced understanding of context and temporality. This could pave the way for AI systems capable of more nuanced and human-like reasoning and interaction.
Conclusion
Larimar represents a significant step forward in the effort to create more dynamic and adaptable LLMs. By successfully integrating an episodic memory that enables real-time knowledge updates, Larimar addresses a critical pain point in the use of LLMs, particularly in applications requiring up-to-date information. As future work builds on and refines this approach, the goal of developing AI systems with the ability to learn and forget as efficiently as humans do appears increasingly attainable.