In-Memory Learning: A Declarative Learning Framework for Large Language Models

arXiv:2403.02757v1 Announce Type: new
Abstract: 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 …

abstract agents alignment arxiv cs.cl data environment exploration framework human in-memory inspiration intelligent language language models large language large language models memory novel pivotal process research role summarizing type