Human-like Episodic Memory for Infinite Context LLMs
Large language models (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.
66948c1b3cd89359e4452143
Zafeirios Fountas
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2024-10-23T07:35:21.165Z
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Zafeirios Fountas
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zfountas
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Martin A Benfeghoul
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2024-07-16T20:16:25.146Z
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Martin Antoine Benfeghoul
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Adnan Oomerjee
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Fenia Christopoulou
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2024-07-15T13:34:42.398Z
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Fenia Christopoulou
user
fenchri
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Gerasimos Lampouras
claimed_verified
2024-07-31T12:49:37.253Z
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Gerasimos (Makis) Lampouras
user
glampouras
66948c1b3cd89359e4452148
Haitham Bou-Ammar
claimed_verified
2024-09-23T13:07:15.120Z
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Haitham Bou Ammar
user
hba123
66948c1b3cd89359e4452149
Jun Wang
Human-like Episodic Memory for Infinite Context LLMs
Large language models (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.
EM-LLM enhances LLMs by integrating human episodic memory principles, enabling efficient handling of extensive contexts through coherent event segmentation and retrieval.
LLMs EM-LLM Bayesian surprise graph-theoretic boundary refinement episodic memory event cognition on-line fashion two-stage memory process similarity-based retrieval temporally contiguous retrieval LongBench dataset InfLLM PassageRetrieval