Entropy-Guided Attention for Private LLMs
Authors: name: Nandan Kumar Jha, name: Brandon Reagen

The pervasiveness of proprietary language models 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 language models, 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{entropy-guided-llm}.