Entropy-Guided Attention: A New Approach for Private Language Models

The use of proprietary language models increasingly raises data privacy concerns. Private Inference (PI) offers a promising solution, as computations are performed directly on encrypted data without revealing sensitive information. However, the practical application of PI is hampered by high communication and latency costs, mainly caused by non-linear operations. A new research approach based on Shannon entropy could overcome these hurdles and enable the development of more efficient architectures for private language models.

The Dual Role of Non-linearities

Non-linear operations play a crucial, yet under-explored, dual role in decoder-based language models. They not only ensure the stability of training but also maintain the diversity of the attention heads. These "attention heads" are responsible for processing and weighting different parts of the input text. Removing non-linearities leads to two critical problems:

Entropy Collapse: In deeper layers of the model, entropy collapse occurs, destabilizing training and impairing learning ability. The predictability of the attention heads increases significantly, limiting the model's performance.

Entropic Overload: In the earlier layers, the removal of non-linearities leads to entropic overload. A disproportionately large number of attention heads are in a state of high entropy, resulting in underutilization of the representational capacity of the Multi-Head Attention (MHA) mechanism. The available resources are not used optimally.

Entropy-Guided Attention and Regularization

To mitigate entropic overload, an entropy-guided attention mechanism was developed in combination with a novel entropy regularization technique. This technique dynamically adjusts the regularization strength to the specific roles of individual attention heads, reducing the dependence on computationally intensive non-linear operations. Additionally, PI-friendly alternatives to layer normalization were explored to prevent entropy collapse and stabilize the training of language models with reduced non-linearities.

Bridging Information Theory and Architectural Design

This research connects information theory and the architectural design of language models. Entropy dynamics serve as the basis for developing efficient PI architectures. This approach promises to advance the development of private language models that are both powerful and privacy-preserving. Especially for companies like Mindverse, which develop customized AI solutions, this approach offers new opportunities to protect sensitive data while leveraging the benefits of state-of-the-art language models.

For Mindverse, which acts as an AI partner and develops customized solutions such as chatbots, voicebots, AI search engines, and knowledge systems, research in the area of data privacy is of particular importance. Entropy-guided attention could be a key component for future developments to meet the increasing demands for data privacy and security.

Bibliography Jha, Nandan Kumar, and Brandon Reagen. "Entropy-Guided Attention for Private LLMs." arXiv preprint arXiv:2501.03489 (2025). https://arxiv.org/abs/2501.03489 https://arxiv.org/pdf/2501.03489 https://www.youtube.com/watch?v=3sgVVcc5_d4 https://creators.spotify.com/pod/show/arxiv-papers/episodes/Entropy-Guided-Attention-for-Private-LLMs-e2t75ld https://creators.spotify.com/pod/show/arxiv-papers/episodes/QA-Entropy-Guided-Attention-for-Private-LLMs-e2t75ll https://www.facebook.com/groups/DeepNetGroup/posts/2377133026012899/ https://huggingface.co/papers https://paperswithcode.com/latest?page=3 https://ppai-workshop.github.io/ https://www.facebook.com/groups/DeepNetGroup/posts/2373341809725354/