title: A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts
summary: Current Large Language Models (LLMs) are not only limited to some maximum
  context length, but also are not able to robustly consume long inputs. To
  address these limitations, we propose ReadAgent, an LLM agent system that
  increases effective context length up to 20x in our experiments. Inspired by
  how humans interactively read long documents, we implement ReadAgent as a
  simple prompting system that uses the advanced language capabilities of LLMs to
  (1) decide what content to store together in a memory episode, (2) compress
  those memory episodes into short episodic memories called gist memories, and
  (3) take actions to look up passages in the original text if ReadAgent needs to
  remind itself of relevant details to complete a task. We evaluate ReadAgent
  against baselines using retrieval methods, using the original long contexts,
  and using the gist memories. These evaluations are performed on three
  long-document reading comprehension tasks: QuALITY, NarrativeQA, and QMSum.
  ReadAgent outperforms the baselines on all three tasks while extending the
  effective context window by 3-20x.
paper_authors._id: 65ced76da0f4d749b2e6f9cd
paper_authors.name: Kuang-Huei Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-20T13:17:48.082Z
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paper_authors.avatarUrl: /avatars/2023c01b2a8cc1625cafcd0b625871dc.svg
paper_authors.fullname: Kuang-Huei Lee
paper_authors.type: user
paper_authors.user: khlee112
paper_authors._id: 65ced76da0f4d749b2e6f9ce
paper_authors.name: Xinyun Chen
paper_authors.status: admin_assigned
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paper_authors._id: 64d0268001931c60161e026a
paper_authors.avatarUrl: /avatars/0ea48c47b0270b449fd6b97b495e64a6.svg
paper_authors.fullname: Xinyun Chen
paper_authors.type: user
paper_authors.user: xinyunchen
paper_authors._id: 65ced76da0f4d749b2e6f9cf
paper_authors.name: Hiroki Furuta
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-16T11:10:02.407Z
paper_authors._id: 646c39fc3e2a7b06594bac6c
paper_authors.avatarUrl: /avatars/8925fef2b6e7105a0f0a4bbf160cfda3.svg
paper_authors.fullname: Hiroki Furuta
paper_authors.type: user
paper_authors.user: frt03
paper_authors._id: 65ced76da0f4d749b2e6f9d0
paper_authors.name: John Canny
paper_authors._id: 65ced76da0f4d749b2e6f9d1
paper_authors.name: Ian Fischer
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-16T11:10:15.054Z
paper_authors._id: 64ef4f9866f36326b3ec5b8c
paper_authors.avatarUrl: /avatars/9eea179e0797f952664f33e1aef21e88.svg
paper_authors.fullname: Ian Fischer
paper_authors.type: user
paper_authors.user: Ianfischer
paper_title: A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts
paper_summary: Current Large Language Models (LLMs) are not only limited to some maximum
  context length, but also are not able to robustly consume long inputs. To
  address these limitations, we propose ReadAgent, an LLM agent system that
  increases effective context length up to 20x in our experiments. Inspired by
  how humans interactively read long documents, we implement ReadAgent as a
  simple prompting system that uses the advanced language capabilities of LLMs to
  (1) decide what content to store together in a memory episode, (2) compress
  those memory episodes into short episodic memories called gist memories, and
  (3) take actions to look up passages in the original text if ReadAgent needs to
  remind itself of relevant details to complete a task. We evaluate ReadAgent
  against baselines using retrieval methods, using the original long contexts,
  and using the gist memories. These evaluations are performed on three
  long-document reading comprehension tasks: QuALITY, NarrativeQA, and QMSum.
  ReadAgent outperforms the baselines on all three tasks while extending the
  effective context window by 3-20x.
paper_ai_summary: ReadAgent, an LLM-based prompting system, enhances effective context length up to 20x through memory episodes and gist memories, outperforming baselines on long-document reading comprehension tasks.
paper_ai_keywords: ReadAgent large language models (LLMs) memory episodes gist memories long-document reading comprehension QuALITY NarrativeQA QMSum