  Recent large language model applications, such as Retrieval-Augmented
Generation and chatbots, have led to an increased need to process longer input
contexts. However, this requirement is hampered by inherent limitations.
Architecturally, models are constrained by a context window defined during
training. Additionally, processing extensive texts requires substantial GPU
memory. We propose a novel approach, Finch, to compress the input context by
leveraging the pre-trained model weights of the self-attention. Given a prompt
and a long text, Finch iteratively identifies the most relevant Key (K) and
Value (V) pairs over chunks of the text conditioned on the prompt. Only such
pairs are stored in the KV cache, which, within the space constrained by the
context window, ultimately contains a compressed version of the long text. Our
proposal enables models to consume large inputs even with high compression (up
to 93x) while preserving semantic integrity without the need for fine-tuning.
