Entropy-Guided Attention for Private LLMs (2501.03489v2)

Abstract: The pervasiveness of proprietary LLMs has raised critical privacy concerns, necessitating advancements in private inference (PI), where computations are performed directly on encrypted data without revealing users' sensitive information. While PI offers a promising solution, its practical deployment is hindered by substantial communication and latency overheads, primarily stemming from nonlinear operations. To address this, we introduce an information-theoretic framework to characterize the role of nonlinearities in decoder-only LLMs, laying a principled foundation for optimizing transformer-architectures tailored to the demands of PI. By leveraging Shannon's entropy as a quantitative measure, we uncover the previously unexplored dual significance of nonlinearities: beyond ensuring training stability, they are crucial for maintaining attention head diversity. Specifically, we find that their removal triggers two critical failure modes: {\em entropy collapse} in deeper layers that destabilizes training, and {\em entropic overload} in earlier layers that leads to under-utilization of Multi-Head Attention's (MHA) representational capacity. We propose an entropy-guided attention mechanism paired with a novel entropy regularization technique to mitigate entropic overload. Additionally, we explore PI-friendly alternatives to layer normalization for preventing entropy collapse and stabilizing the training of LLMs with reduced-nonlinearities. Our study bridges the gap between information theory and architectural design, establishing entropy dynamics as a principled guide for developing efficient PI architectures. The code and implementation are available at https://github.com/Nandan91/entropy-guided-attention-LLM

• The paper presents an entropy-guided attention mechanism that tackles both entropy collapse and overload in transformer nonlinearities.
• It introduces entropy regularization and PI-friendly alternatives to layer normalization, significantly reducing computational overhead.
• Empirical results demonstrate a 3.94× reduction in overhead and a 7.8% improvement in perplexity, underscoring its practical impact.

Entropy-Guided Attention for Private LLMs

The paper "Entropy-Guided Attention for Private LLMs" addresses critical privacy challenges in the deployment of proprietary LLMs. These challenges arise due to the large computational overheads associated with nonlinear operations required for private inference (PI) in transformer-based architectures. The authors introduce an innovative information-theoretic framework that utilizes Shannon's entropy to optimize the architectural design of transformers for PI, through an in-depth analysis of the role of nonlinearities.

Key Contributions

1. Dual Role of Nonlinearities: The paper reveals that nonlinearities in transformer architectures are essential not only for training stability but also for maintaining attention head diversity. The authors identify two key failure modes, namely entropy collapse in deeper layers and entropic overload in earlier layers, when these nonlinearities are removed.
2. Entropy-Guided Mechanisms: The paper introduces entropy-guided attention mechanisms paired with a new entropy regularization technique. These innovations are aimed at mitigating entropic overload and preventing entropy collapse, thereby enhancing the training and performance of transformers in environments with limited nonlinear components.
3. PI-friendly Alternatives: The authors explore PI-compatible alternatives to layer normalization, employing static normalization techniques like weight and spectral normalization. These methods provide stabilization without relying on traditional, computationally expensive layer normalization.
4. Practical Implementation: The proposed mechanisms are evaluated on various transformer models, highlighting their effectiveness in reducing communication and latency overheads while maintaining performance. This is demonstrated through experiments on models like GPT-2, trained on datasets such as CodeParrot and Languini.

Numerical Results

The paper reports a significant reduction in PI-related communication overhead, achieving a 3.94× reduction alongside a 1.72× speedup in latency for a simplified GPT-2 model. Further enhancements in model performance are quantified by a 7.8% improvement in perplexity, achieved through the entropy regularization technique. These results establish the proposed framework as a viable solution for enhancing the efficiency of PI in transformer-based models.

Theoretical and Practical Implications

The paper bridges the gap between information theory and neural network architecture by establishing entropy dynamics as a critical factor in the design of efficient, privacy-preserving LLM architectures. This approach provides a new perspective on regularizing transformer networks, emphasizing entropy as a tool for balancing computational efficiency and model performance.

On the practical side, the paper demonstrates that substantial improvements in PI can be realized without drastic changes to the underlying model architecture, solely by manipulating entropy. This positions the framework as a practical guide for implementing secure and efficient LLM inferences.

Future Directions

Future advancements in this area may explore further integration of entropy-based strategies to other parts of the transformer architectures, potentially leveraging adaptive architectures that dynamically adjust entropy thresholds during operation. Additionally, extending these findings to larger models and more diverse usage scenarios will be crucial for broadening the applicability of the proposed methodologies.

In summary, this paper presents a methodologically rigorous and practically impactful contribution to the field of secure and efficient LLM deployment. Its use of entropy as both an analytic lens and a regulatory mechanism opens new avenues for optimizing model architectures under privacy constraints.