  We propose the In-context Autoencoder (ICAE), leveraging the power of a large
language model (LLM) to compress a long context into short compact memory slots
that can be directly conditioned on by the LLM for various purposes. ICAE is
first pretrained using both autoencoding and language modeling objectives on
massive text data, enabling it to generate memory slots that accurately and
comprehensively represent the original context. Then, it is fine-tuned on
instruction data for producing desirable responses to various prompts.
Experiments demonstrate that our lightweight ICAE, introducing about 1%
additional parameters, effectively achieves $4\times$ context compression based
on Llama, offering advantages in both improved latency and GPU memory cost
during inference, and showing an interesting insight in memorization as well as
potential for scalability. These promising results imply a novel perspective on
the connection between working memory in cognitive science and representation
learning in LLMs, revealing ICAE's significant implications in addressing the
long context problem and suggesting further research in LLM context management.
Our data, code and models are available at https://github.com/getao/icae.
