zkLLM: Zero Knowledge Proofs for Large Language Models (2404.16109v1)

Abstract: The recent surge in AI, characterized by the prominence of LLMs, has ushered in fundamental transformations across the globe. However, alongside these advancements, concerns surrounding the legitimacy of LLMs have grown, posing legal challenges to their extensive applications. Compounding these concerns, the parameters of LLMs are often treated as intellectual property, restricting direct investigations. In this study, we address a fundamental challenge within the realm of AI legislation: the need to establish the authenticity of outputs generated by LLMs. To tackle this issue, we present zkLLM, which stands as the inaugural specialized zero-knowledge proof tailored for LLMs to the best of our knowledge. Addressing the persistent challenge of non-arithmetic operations in deep learning, we introduce tlookup, a parallelized lookup argument designed for non-arithmetic tensor operations in deep learning, offering a solution with no asymptotic overhead. Furthermore, leveraging the foundation of tlookup, we introduce zkAttn, a specialized zero-knowledge proof crafted for the attention mechanism, carefully balancing considerations of running time, memory usage, and accuracy. Empowered by our fully parallelized CUDA implementation, zkLLM emerges as a significant stride towards achieving efficient zero-knowledge verifiable computations over LLMs. Remarkably, for LLMs boasting 13 billion parameters, our approach enables the generation of a correctness proof for the entire inference process in under 15 minutes. The resulting proof, compactly sized at less than 200 kB, is designed to uphold the privacy of the model parameters, ensuring no inadvertent information leakage.

• The paper introduces a novel ZKP framework for LLMs, incorporating tlookup for non-arithmetic operations and zkAttn for attention mechanism verification.
• Its CUDA-based implementation demonstrates efficient proof generation in 15 minutes for models up to 13 billion parameters with proofs under 200kB.
• This framework facilitates secure, private LLM output validation, meeting stringent regulatory and proprietary requirements in high-stakes domains.

zkLLM: Zero-Knowledge Proofs for LLM Legitimacy Verification

Introduction

The paper introduces zkLLM, a novel zero-knowledge proof (ZKP) framework designed for LLMs, tackling significant challenges related to the legitimacy verification of outputs from such models. The necessity for robust verification solutions has been driven by increasing regulatory scrutiny and the critical proprietary nature of LLMs. The authors present new techniques, namely tlookup and zkAttn, addressing the complex non-arithmetic and attention mechanisms inherent in LLMs respectively.

Key Contributions and Techniques

The contributions of this research are substantial in progressing the practical application of ZKPs in the domain of LLMs:

1. tlookup: Efficient Non-Arithmetic Verification
   • Created to efficiently handle non-arithmetic operations prevalent in neural network activation functions.
   • Utilizes parallelized lookup arguments specific for tensor operations, achieving enhanced performance by eliminating asymptotic overheads.
2. zkAttn: Specialized Zero-Knowledge Proof for Attention Mechanisms
   • Addresses the specific complexities of the attention mechanism integral to transformer-based LLMs.
   • Balances proof overhead, running time, and accuracy without compromising on security standards.
3. Practical Implementation and Results
   • Delivered through a CUDA-based implementation showcasing the viability of zkLLM on up to 13 billion parameter models.
   • Demonstrated proof generation within 15 minutes for state-of-the-art LLMs, with proofs compacted to less than 200kB ensuring quick verification times of 1-3 seconds.

Theoretical and Practical Implications

From a theoretical perspective, the zkAttn component is particularly noteworthy. It adapys zero-knowledge proofs for the inherently multivariate and non-arithmetic Softmax function used in attention layers. The design and implementation effectively counter the challenges posed by high dimensionality and non-linearity of operations in LLMs.

Practically, zkLLM positions itself as a cornerstone for future developments in secure AI. It enables entities to verify model outputs without exposure of proprietary parameters, thus aligning with legal and privacy standards. These advancements could significantly ease the deployment of LLMs in environments where data privacy and security are paramount, such as healthcare and governmental applications.

Future Research Directions

Further explorations could look into optimizing the efficiency of tlookup operations and expanding the zkLLM framework's applicability to other complex AI model architectures. Moreover, investigating the integration of zkLLM with federated learning environments could also provide a new pathway for secure, decentralized machine learning applications.

Another potential area of future work is the adaptation of these techniques into training phases of LLMs, although the feasibility and practicality of such an application remain to be thoroughly investigated considering the immense computational resources required for training these models.

Conclusion

zkLLM represents a significant advance in the application of zero-knowledge proofs to large-scale machine learning models. By addressing specific technical challenges with innovative solutions, this work not only enhances the practical deployment of secure and verified AI but also opens up new avenues for research and development in the field of zero-knowledge machine learning.