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The paper introduces Echo, a large language model enhanced with temporal episodic memory, to address the limitations of existing LLMs in handling episodic memory-related queries. They propose a Multi-Agent Data Generation Framework to create a multi-turn, complex scenario episodic memory dialogue dataset (EM-Train) and incorporate temporal information into the LLM training process. Experiments on a newly developed EM-Test benchmark demonstrate that Echo significantly outperforms state-of-the-art LLMs in episodic memory tasks, suggesting potential for human-like episodic memory capabilities.
LLMs can now remember the past: Echo surpasses existing models in episodic memory tasks by incorporating temporal information into training data generation and model architecture.
Research on large language models (LLMs) has shown remarkable performance in domains such as mathematics, programming, and literary creation. However, most studies have focused on semantic memory-based question answering, neglecting LLMs' potential to handle episodic memory (EM)-related queries. This oversight has led to suboptimal performance in applications requiring EM, including emotional companionship, personal AI assistants, and AI teachers. To address this gap, we introduce Echo, a LLM enhanced with temporal episodic memory. We propose a Multi-Agent Data Generation Framework that guides the model in generating multi-turn, complex scenario episodic memory dialogue data (EM-Train). Temporal information is innovatively incorporated into the LLM training process, and Echo is trained using the EM-Train. Furthermore, We develop an EM-Test benchmark specifically designed to evaluate LLMs' episodic memory capabilities. The EM-Test assesses performance across various time spans and difficulty levels, providing a comprehensive evaluation of multi-turn episodic memory dialogues. Our experiments demonstrate that Echo significantly outperforms state-of-the-art LLMs on EM-Test. Additionally, a qualitative analysis reveals Echo's potential to exhibit human-like episodic memory capabilities. We will open-source all datasets, code, and model weights.
