Can LLMs Maintain Fundamental Abilities under KV Cache Compression?

Xiang Liu, Zhenheng Tang, Hong Chen, PeijieDong, Zeyu Li, Xiuze Zhou, Bo Li, Xuming Hu, Xiaowen Chu

Can LLMs Maintain Fundamental Abilities under KV Cache Compression?: 13 upvotes on Hugging Face Daily Papers, #6 of 14 papers on 2025-02-05. Day-by-day upvote history.

This paper investigates an under-explored challenge in large language models (LLMs): the impact of KV cache compression methods on LLMs' fundamental capabilities. While existing methods achieve impressive compression ratios on long-context benchmarks, their effects on core model capabilities remain understudied. We present a comprehensive empirical study evaluating prominent KV cache compression methods across diverse tasks, spanning world knowledge, commonsense reasoning, arithmetic reasoning, code generation, safety, and long-context understanding and generation.Our analysis reveals that KV cache compression methods exhibit task-specific performance degradation. Arithmetic reasoning tasks prove particularly sensitive to aggressive compression, with different methods showing performance drops of 17.4%-43.3%. Notably, the DeepSeek R1 Distill model exhibits more robust compression tolerance compared to instruction-tuned models, showing only 9.67%-25.53% performance degradation. Based on our analysis of attention patterns and cross-task compression performance, we propose ShotKV, a novel compression approach that distinctly handles prefill and decoding phases while maintaining shot-level semantic coherence. Empirical results show that ShotKV achieves 9%-18% performance improvements on long-context generation tasks under aggressive compression ratios.

Paper page on Hugging Face · arXiv

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