Large-scale Language Models (LLMs) are constrained by their inability to
process lengthy inputs. To address this limitation, we propose the
Self-Controlled Memory (SCM) system to unleash infinite-length input capacity
for large-scale language models. Our SCM system is composed of three key
modules: the language model agent, the memory stream, and the memory
controller. The language model agent iteratively processes ultra-long inputs
and stores all historical information in the memory stream. The memory
controller provides the agent with both long-term memory (archived memory) and
short-term memory (flash memory) to generate precise and coherent responses.
The controller determines which memories from archived memory should be
activated and how to incorporate them into the model input. Our SCM system can
be integrated with any LLMs to enable them to process ultra-long texts without
any modification or fine-tuning. Experimental results show that our SCM system
enables LLMs, which are not optimized for multi-turn dialogue, to achieve
multi-turn dialogue capabilities that are comparable to ChatGPT, and to
outperform ChatGPT in scenarios involving ultra-long document summarization or
long-term conversations. Additionally, we will supply a test set, which covers
common long-text input scenarios, for evaluating the abilities of LLMs in
processing long documents.~\footnote{Working in
progress.}\footnote{\url{https://github.com/wbbeyourself/SCM4LLMs}}Comment: Working in progres