A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts
Authors: name: Kuang-Huei Lee, name: Xinyun Chen, name: Hiroki Furuta, name: John Canny, name: Ian Fischer

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.