KV-Distill: Nearly Lossless Learnable Context Compression for LLMs (2503.10337v1)

Abstract: Sequence-to-sequence tasks often benefit from long contexts, but the quadratic complexity of self-attention in standard Transformers renders this non-trivial. During generation, temporary representations -stored in the so-called KV cache-account for a large portion of GPU memory usage and scale linearly with context length. We introduce KV-Distill, a Transformer compression framework that distills long context KV caches into significantly shorter representations in a question-independent fashion. KV-Distill can be trained as a parameter-efficient adaptor for pretrained models, and enables the compression of arbitrary spans of a context while preserving pre-trained model capabilities. We treat a compressed-uncompressed cache as a student-teacher pairing and apply a KL-type divergence to match the generated outputs. KV-Distill outperforms other compression techniques in worst-case extractive tasks and approaches uncompressed performance in long context question answering and summarization, and it can be fine-tuned on domain-specific contexts to reduce lengths by up to 99% while preserving downstream performance. We demonstrate the generalizability of KV-Distill across various model sizes and architectures.

kv-distill: Near-Lossless Context Compression for LLMs

The paper "kv-distill: Nearly Lossless Learnable Context Compression for LLMs" addresses a critical challenge in the deployment of LLMs within sequence-to-sequence tasks, specifically the inefficiencies presented by the quadratic complexity inherent in the self-attention mechanism of Transformer architectures. This inefficiency complicates the handling of expansive contexts. The kv-distill framework introduced by Chari, Qin, and Van Durme aims to resolve these inefficiencies by minimizing the memory usage during generation without sacrificing model performance.

Overview of kv-distill Framework

The kv-distill framework proposes a method for compressing the temporary key-value (kv) cache, which grows linearly with context length and poses significant memory burdens during LLM operation. By treating the compressed-uncompressed kv cache pairings as a student-teacher configuration, the framework applies a KL-type divergence to align the outputs generated by the compressed cache with those from the uncompressed cache. This approach outperforms existing context compression techniques, notably in extractive tasks, while maintaining near-uncompressed performance levels in long context scenarios such as question answering and summarization. Moreover, kv-distill allows for context compression rates of up to 99% with minimal impact on downstream tasks.

Technical Contributions and Methodology

Key contributions of this paper are articulated through several methodological innovations:

• Question-dependent and Question-independent Compression Paradigms: The paper delineates compression techniques into two paradigms, addressing the potential reuse of compressed contexts across various questions.
• Parameter-Efficient Adaptation: kv-distill can be trained as an adaptor for existing pre-trained models, which implies a reduction in computational overhead and efficient utilization of existing model capabilities.
• Generalizability Across Model Architectures: Experiments conducted demonstrate the framework’s applicability and efficacy across different sizes and types of LLMs, which is essential for widespread adoption in AI deployments.

The architecture framework tests various scoring mechanisms for token importance within the kv cache, operating as a filter to optimize the compression of necessary context information. Conditional computation informs models about which tokens are retained, enhancing the aggregation of value representations for selected tokens. This functionality is parameterized, ensuring efficiency in training and inference phases.

Implications and Future Work

Implications of this research are vast in both practical and theoretical realms. Practically, kv-distill offers significant reductions in memory usage, enabling the handling of longer contexts in LLMs without substantial performance degradation. From a theoretical perspective, the insights gained from employing KL-type divergence for context compression open avenues for further research into adaptive memory management and information retention mechanisms within Transformer models.

Future developments may explore:

• Exploration of Alternative Token Scoring Functions: Although the current scoring mechanism yields good results, there is potential for further refinement using other configurations.
• Expansion Beyond Transformer Architectures: While this paper focuses on Transformer models, the principles of kv compression could be extended to other architectures to assess universal applicability.
• Improvement of Compression Tolerance: The boundary testing of kv-distill compression levels points toward optimizing performance metrics under extreme constraints without loss of fidelity.

Conclusion

Overall, the kv-distill framework represents a substantial advancement in efficient large-scale model deployment, offering a strategic approach to mitigating key-value cache limitations inherent in self-attention mechanisms. Its capacity to adaptively compress context while preserving model functionality positions it as a viable solution for future integration in LLM-driven applications. The paper’s findings reinforce the potential benefits of targeted context compression, advocating for further exploration into model efficiencies in real-world settings.