title: Human-like Episodic Memory for Infinite Context LLMs
summary: 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.
paper_authors._id: 66948c1b3cd89359e4452143
paper_authors.name: Zafeirios Fountas
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paper_authors.fullname: Zafeirios Fountas
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paper_authors.name: Martin A Benfeghoul
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paper_authors.fullname: Martin Antoine Benfeghoul
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paper_authors.user: m84366023
paper_authors._id: 66948c1b3cd89359e4452145
paper_authors.name: Adnan Oomerjee
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paper_authors.name: Fenia Christopoulou
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paper_authors.fullname: Fenia Christopoulou
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paper_authors.user: fenchri
paper_authors._id: 66948c1b3cd89359e4452147
paper_authors.name: Gerasimos Lampouras
paper_authors.status: claimed_verified
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paper_authors.fullname: Gerasimos (Makis) Lampouras
paper_authors.type: user
paper_authors.user: glampouras
paper_authors._id: 66948c1b3cd89359e4452148
paper_authors.name: Haitham Bou-Ammar
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-23T13:07:15.120Z
paper_authors._id: 631c375768f7da9ad2496bf6
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paper_authors.fullname: Haitham Bou Ammar
paper_authors.type: user
paper_authors.user: hba123
paper_authors._id: 66948c1b3cd89359e4452149
paper_authors.name: Jun Wang
paper_title: Human-like Episodic Memory for Infinite Context LLMs
paper_summary: 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.
paper_ai_keywords: Bayesian surprise graph-theoretic boundary refinement episodic events similarity-based retrieval temporally contiguous retrieval event segmentation EM-LLM LongBench dataset InfLLM model PassageRetrieval task