FreqKV: Frequency Domain Key-Value Compression for Efficient Context Window Extension

• URL: http://arxiv.org/abs/2505.00570v2
• Date: Mon, 19 May 2025 02:21:16 GMT
• Title: FreqKV: Frequency Domain Key-Value Compression for Efficient Context Window Extension
• Authors: Jushi Kai, Boyi Zeng, Yixuan Wang, Haoli Bai, Ziwei He, Bo Jiang, Zhouhan Lin,
• Abstract summary: We propose FreqKV, a novel frequency domain key-value ( KV) compression technique.<n>Freq KV enables efficient context window extension for decoder-only large language models (LLMs)<n> Experiments on a range of long context language modeling and understanding tasks demonstrate the efficiency and effectiveness of the proposed method.
• Score: 20.360392907997117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Frequency-domain compression has proven effective in reducing redundancies for spatial signals. In this work, we propose FreqKV, a novel frequency domain key-value (KV) compression technique that enables efficient context window extension for decoder-only large language models (LLMs). Our approach is motivated by a key observation that, in the frequency domain, the energy distribution of the KV cache is predominantly concentrated in low-frequency components. By discarding high-frequency components, we achieve efficient compression of the KV cache with minimal information loss. FreqKV iteratively compresses the increasing KV cache to a fixed size in the frequency domain, allowing models to process lengthy contexts efficiently. Introducing no additional parameters or architectural modifications, FreqKV is applicable to both fine-tuning and inference. With minimal fine-tuning, LLMs can learn to leverage the limited cache that is compressed in the frequency domain and extend the context window. Experiments on a range of long context language modeling and understanding tasks demonstrate the efficiency and effectiveness of the proposed method.

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