Can LLMs Maintain Fundamental Abilities under KV Cache Compression? (2502.01941v2)

Abstract: This paper investigates an underexplored challenge in LLMs: the impact of KV cache compression methods on LLMs' fundamental capabilities. Although existing methods achieve impressive compression ratios on long-context benchmarks, their effects on core model capabilities remain understudied. We present a comprehensive benchmark KVFundaBench to systematically evaluate the effects of KV cache compression across diverse fundamental LLM capabilities, spanning world knowledge, commonsense reasoning, arithmetic reasoning, code generation, safety, and long-context understanding and generation.Our analysis reveals serval key findings: (1) \textit{Task-Dependent Degradation}; (2) \textit{Model-Type Robustness} (3) \textit{Prompt Length Vulnerability}; (4) \textit{Chunk-Level Superiority}; (5) \textit{Prompt-Gain Sensitivity}; (6) \textit{Long-Context Generation Sensitivity}. 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.

• The paper empirically investigates how KV cache compression impacts Large Language Model capabilities across diverse fundamental tasks and prompt lengths.
• Findings reveal significant task-specific degradation, particularly in arithmetic reasoning (17.4% to 43.3% loss), while long-context tasks are more tolerant and short prompts more sensitive.
• The novel ShotKV method is proposed, enhancing performance under aggressive compression by up to 18% in long-context tasks through improved prefill and decoding phase management.

An Analysis of KV Cache Compression on LLMs

The paper "Can LLMs Maintain Fundamental Abilities under KV Cache Compression?" by Liu et al. presents a thorough empirical investigation into the effects of Key-Value (KV) cache compression on LLMs and their fundamental capabilities. This research addresses a significant challenge in the deployment of LLMs—namely, the increasing GPU memory requirements during inference as context lengths extend. The paper evaluates various KV cache compression methods across different tasks and proposes a novel approach, ShotKV, to improve performance during aggressive compression settings.

LLMs have excelled in handling tasks requiring significant context lengths, such as question answering and summarization, yet these capabilities often lead to prohibitive computational costs. The paper focuses on key insights regarding the task-dependent performance degradation due to KV cache compression and how it affects various core model capabilities such as arithmetic, commonsense, and code generation.

Key Findings

1. Task-Specific Performance Degradation: The compression methods evaluated in the paper show varying effects on different tasks. Notably, arithmetic reasoning tasks exhibit pronounced sensitivity, with performance losses between 17.4% to 43.3% under aggressive compression. In contrast, long-context understanding shows more tolerant responses to compression.
2. Robustness of Multi-Step Reasoning LLMs: The R1-Distill-Llama models demonstrate greater robustness compared to instruction-tuned LLMs. Notably, they show reduced performance degradation down to 9.67%-25.53%, emphasizing that certain architectures are intrinsically more compression-resistant.
3. Impact of Prompt Lengths: Short prompts exhibit greater sensitivity to KV cache compression. The experiments illustrate that longer prompts provide a buffer against the severe degradation that shorter prompts suffer under aggressive compression.
4. Chunk-level Compression Superiority: On complex tasks such as long-context arithmetic reasoning, strategies like ChunkKV outperform others by maintaining higher semantic coherence through chunk-level retention.
5. Sensitivity in Tasks with Larger Prompt-Based Gains: Tasks showing significant performance improvement with prompt-based inputs are more sensitive to the compression of those prompts. This highlights the need to tailor compression strategies according to task-specific dynamics.
6. Emergence of ShotKV: The novel ShotKV method, distinctively managing prefill and decoding phases, presents an improvement in handling aggressive compression ratios by up to 18% in long-context generation tasks. The methodology emphasizes maintaining shot-level semantic integrity, proving advantageous in tasks demanding extended reasoning.

Theoretical and Practical Implications

The findings offer critical insights into the trade-offs between memory efficiency and sustaining model performance across diverse tasks. Practically, these insights emphasize the necessity of task-specific KV cache compression strategies, especially for deploying LLMs in environments with constrained resources. The paper encourages further examination of compression-aware model training strategies to enhance resilience against compression-induced degradation. This has implications for both the design of future LLM architectures and the development of adaptive compression strategies that can dynamically adjust based on task requirements and context complexity.

Future Directions

The paper sets the stage for ongoing research into improving the efficiency of LLM deployments while retaining their comprehensive abilities. Future research could explore adaptive compression frameworks that respond to task-specific needs and context lengths. Additionally, the development of training approaches that incorporate compression objectives could further enhance model robustness against compression. Extending the findings to other model architectures and evaluating across more diverse tasks could yield even broader applications and optimizations in the field of AI.

Conclusion

Overall, this paper offers a substantial contribution to understanding how KV cache compression techniques impact LLMs' fundamental abilities and the design of methods like ShotKV for mitigating performance degradation. The research underscores the importance of task specificity in KV cache compression strategies and provides a robust foundation for further advancements in AI efficiency optimization.