In-Memory Learning: A Declarative Learning Framework for Large Language
  Models
Bo Wang Tianxiang Sun Hang Yan Siyin Wang Qingyuan Cheng Xipeng Qiu
The exploration of whether agents can align with their environment without
relying on human-labeled data presents an intriguing research topic. Drawing
inspiration from the alignment process observed in intelligent organisms, where
declarative memory plays a pivotal role in summarizing past experiences, we
propose a novel learning framework. The agents adeptly distill insights from
past experiences, refining and updating existing notes to enhance their
performance in the environment. This entire process transpires within the
memory components and is implemented through natural language, so we character
this framework as In-memory Learning. We also delve into the key features of
benchmarks designed to evaluate the self-improvement process. Through
systematic experiments, we demonstrate the effectiveness of our framework and
provide insights into this problem.