  With the rapid development of large language models, AI assistants like
ChatGPT have widely entered people's works and lives. In this paper, we present
an evolving large language model assistant that utilizes verbal long-term
memory. It focuses on preserving the knowledge and experience from the history
dialogue between the user and AI assistant, which can be applied to future
dialogue for generating a better response. The model generates a set of records
for each finished dialogue and stores them in the memory. In later usage, given
a new user input, the model uses it to retrieve its related memory to improve
the quality of the response. To find the best form of memory, we explore
different ways of constructing the memory and propose a new memorizing
mechanism called conditional memory to solve the problems in previous methods.
We also investigate the retrieval and usage of memory in the generation
process. The assistant uses GPT-4 as the backbone and we evaluate it on three
constructed test datasets focusing on different abilities required by an AI
assistant with long-term memory.
