  Transformer-based Large Language Models (LLMs) often impose limitations on
the length of the text input to ensure the generation of fluent and relevant
responses. This constraint restricts their applicability in scenarios involving
long texts. We propose a novel semantic compression method that enables
generalization to texts that are 6-8 times longer, without incurring
significant computational costs or requiring fine-tuning. Our proposed
framework draws inspiration from source coding in information theory and
employs a pre-trained model to reduce the semantic redundancy of long inputs
before passing them to the LLMs for downstream tasks. Experimental results
demonstrate that our method effectively extends the context window of LLMs
across a range of tasks including question answering, summarization, few-shot
learning, and information retrieval. Furthermore, the proposed semantic
compression method exhibits consistent fluency in text generation while
reducing the associated computational overhead.
