Human-like Episodic Memory for Infinite Context LLMs
(2407.09450)Abstract
LLMs have shown remarkable capabilities, but still struggle with processing extensive contexts, limiting their ability to maintain coherence and accuracy over long sequences. In contrast, the human brain excels at organising and retrieving episodic experiences across vast temporal scales, spanning a lifetime. In this work, we introduce EM-LLM, a novel approach that integrates key aspects of human episodic memory and event cognition into LLMs, enabling them to effectively handle practically infinite context lengths while maintaining computational efficiency. EM-LLM organises sequences of tokens into coherent episodic events using a combination of Bayesian surprise and graph-theoretic boundary refinement in an on-line fashion. When needed, these events are retrieved through a two-stage memory process, combining similarity-based and temporally contiguous retrieval for efficient and human-like access to relevant information. Experiments on the LongBench dataset demonstrate EM-LLM's superior performance, outperforming the state-of-the-art InfLLM model with an overall relative improvement of 4.3% across various tasks, including a 33% improvement on the PassageRetrieval task. Furthermore, our analysis reveals strong correlations between EM-LLM's event segmentation and human-perceived events, suggesting a bridge between this artificial system and its biological counterpart. This work not only advances LLM capabilities in processing extended contexts but also provides a computational framework for exploring human memory mechanisms, opening new avenues for interdisciplinary research in AI and cognitive science.
Overview
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The paper introduces EM-LLM, a novel architecture inspired by human episodic memory, aimed at enabling LLMs to process virtually infinite context lengths effectively.
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EM-LLM organizes sequences into coherent episodic events using Bayesian surprise and graph-theoretic boundary refinement, enabling efficient memory formation and retrieval through similarity-based and temporally contiguous mechanisms.
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EM-LLM outperforms the state-of-the-art InfLLM model on the LongBench benchmark and closely mirrors human memory processes, with significant improvements in long-term context comprehension and event recall.
Human-like Episodic Memory for Infinite Context LLMs: A Summary
The paper "Human-like Episodic Memory for Infinite Context LLMs" introduces an innovative approach to enhance the capabilities of LLMs in processing virtually infinite context lengths. Contemporary LLMs, despite their remarkable advancements, struggle with maintaining coherence and accuracy over extended sequences due to the limitations inherent in Transformer architectures. This work proposes EM-LLM, an architecture inspired by human episodic memory, to address these challenges.
Key Contributions
The primary contribution of this work is the integration of human episodic memory principles into LLMs, enabling efficient handling of extensive contexts. EM-LLM leverages key aspects of human event cognition, organizing sequences of tokens into coherent episodic events using Bayesian surprise combined with graph-theoretic boundary refinement. The model retrieves these events through a two-stage memory process, combining similarity-based and temporally contiguous retrieval mechanisms.
Methodology
Memory Formation
Memory formation in EM-LLM is driven by the concept of surprise, computed as the negative log-likelihood of observing the next token given the previous context. High-surprise tokens identify potential event boundaries. This initial segmentation is refined using graph-theoretic techniques to optimize intra-event cohesion and inter-event separation. Specifically, the adjacency matrix of similarities between attention keys is employed to enhance segment coherence through metrics like modularity and conductance.
Memory Retrieval
Retrieval in EM-LLM involves two buffers: the similarity buffer and the contiguity buffer. The similarity buffer uses k-nearest neighbors (k-NNs) to identify relevant events, while the contiguity buffer ensures retrieval of temporally adjacent events, thereby mimicking the temporal contiguity observed in human memory retrieval. This combination allows LLMs to maintain relevant temporal dynamics and efficiently access pertinent information.
Evaluation and Results
EM-LLM demonstrates significant performance improvements over the state-of-the-art InfLLM model on the LongBench benchmark. Notably, EM-LLM achieves an overall relative improvement of 4.3%, including a substantial 33% improvement on the PassageRetrieval task, which involves identifying the original paragraph from a summary—a challenging task requiring accurate long-term memory recall.
The boundary refinement process, evaluated using graph-theoretic metrics, shows superior performance compared to fixed segmentation methods. Additionally, a comparison with human-annotated data reveals that the surprise-based segmentation in EM-LLM aligns closely with human-perceived events, underscoring the model's ability to mimic human cognitive processes.
Implications and Future Directions
The practical implications of EM-LLM are significant. By enabling LLMs to process and utilize information from extended contexts, this approach can enhance applications requiring long-term coherence and detailed recall, such as document summarization, question answering, and conversational agents. Theoretically, EM-LLM provides a computational framework for exploring human memory mechanisms, offering a bridge between cognitive science and artificial intelligence.
Future developments could explore the hierarchical application of surprise-based segmentation across multiple Transformer layers to capture more nuanced event structures. Furthermore, investigating how skewing event recall based on recency and surprise affects model performance could provide deeper insights into memory retrieval dynamics. The potential to extend EM-LLM's principles to multi-modal tasks and its implications for continuous learning and model-based reinforcement learning present exciting avenues for further research.
Conclusion
EM-LLM represents a significant advancement in the development of LLMs with the ability to process virtually infinite context lengths. By drawing on human episodic memory, this model not only addresses the limitations of current Transformer architectures but also opens new frontiers for interdisciplinary research in AI and cognitive science. This approach promises to enhance the capability of LLMs for a wide range of applications, paving the way for more coherent, contextually aware, and human-like artificial intelligence systems.
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