  As Large Language Models (LLMs) become increasingly prevalent in various
domains, their ability to process inputs of any length and maintain a degree of
memory becomes essential. However, the one-off input of overly long texts is
limited, as studies have shown that when input lengths exceed the LLMs'
pre-trained text length, there is a dramatic decline in text generation
capabilities. Moreover, simply extending the length of pre-training texts is
impractical due to the difficulty in obtaining long text data and the
substantial memory consumption costs this would entail for LLMs. Recent efforts
have employed streaming inputs to alleviate the pressure of excessively long
text inputs, but this approach can significantly impair the model's long-term
memory capabilities.
  Motivated by this challenge, we introduce Streaming Infinite Retentive LLM
(SirLLM), which allows LLMs to maintain longer memory during infinite-length
dialogues without the need for fine-tuning. SirLLM utilizes the Token Entropy
metric and a memory decay mechanism to filter key phrases, endowing LLMs with
both long-lasting and flexible memory. We designed three distinct tasks and
constructed three datasets to measure the effectiveness of SirLLM from various
angles: (1) DailyDialog; (2) Grocery Shopping; (3) Rock-Paper-Scissors. Our
experimental results robustly demonstrate that SirLLM can achieve stable and
significant improvements across different LLMs and tasks, compellingly proving
its effectiveness. When having a coversation, "A sir could forget himself," but
SirLLM never does! Our code is publicly available at
https://github.com/Zoeyyao27/SirLLM
