title: Entropy-Guided Attention for Private LLMs
summary: 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}.
paper_authors._id: 678047e05e9a7b1c28c857be
paper_authors.name: Nandan Kumar Jha
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-10T08:36:07.110Z
paper_authors._id: 670ec3f6db1a6bcfe832e0a6
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/-mZNLeLJoXzkwPgYO38lF.png
paper_authors.fullname: Nandan Kumar Jha
paper_authors.type: user
paper_authors.user: nandan523
paper_authors._id: 678047e05e9a7b1c28c857bf
paper_authors.name: Brandon Reagen
paper_title: Entropy-Guided Attention for Private LLMs
paper_summary: 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}.
paper_ai_keywords: private inference (PI) decoder-only language models transformer-architectures Shannon's entropy entropy collapse entropy overload Multi-Head Attention (MHA) entropy-guided attention mechanism entropy regularization technique layer normalization large language models (LLMs) entropy dynamics