Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion

Taylor Unswift: Enhancing Security in LLM Weight Distribution with Taylor Series

In the realm of Large Language Models (LLMs), there exists a distinct division between open and closed models, each catering to different user needs and security paradigms.

Closed vs. Open Large Language Models

Closed LLMs restrict user access to the underlying architecture and weights, functioning primarily through APIs. Users submit their data to these models and receive processed results without direct interaction with the model’s internal mechanisms. Notable examples of closed LLMs include ChatGPT and Claude, where the model weights remain inaccessible to users, preserving the developer's proprietary control but raising concerns about data privacy as users must share sensitive information to obtain results.

Open LLMs, on the other hand, offer complete transparency by sharing the model weights with the public. This openness allows users to operate these models without transmitting their data externally, thus enhancing privacy. Examples include Llama and Mixtral. However, this model poses risks as it enables users to potentially exploit the model for commercial or unethical purposes due to unrestricted access to its weights.

Introducing Taylor Unswift: Secured Weight Release Strategy

To address the challenges posed by both model types, the initiative "Taylor Unswift" proposes a novel method for weight distribution using Taylor series expansions. This approach transforms the original weights of an LLM into a series of parameters derived from Taylor expansions. These transformed parameters are significantly challenging for users to revert to their original form, thus securing the proprietary aspects of the model while still releasing useful components to the public.

Under Taylor Unswift, developers retain control over crucial aspects of the model’s utility, such as the speed of token generation—a control point absent in open LLMs. This method ensures that while users gain access to powerful model capabilities, the core intellectual property remains protected, preventing unauthorized full exploitation of the model’s capabilities.

Conclusion

Taylor Unswift presents a balanced solution to the dilemmas of weight sharing in LLMs, combining the privacy benefits of open LLMs with the control and security of closed models. By applying principles from mathematical Taylor expansions, Taylor Unswift provides a secure, controlled way of releasing LLM weights, thereby mitigating the risks associated with both open and closed systems. This approach promises to reshape the landscape of LLM deployment, making these powerful tools more accessible yet secure.

paper: https://arxiv.org/abs/2410.05331

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