ProFLingo: A Fingerprinting-based Intellectual Property Protection Scheme for Large Language Models
This addresses intellectual property protection for LLM developers and owners, offering a practical solution to verify model ownership in black-box scenarios, though it is incremental as it builds on existing fingerprinting concepts for a new domain.
The paper tackles the problem of unauthorized use or reproduction of large language models (LLMs) by proposing ProFLingo, a black-box fingerprinting scheme that generates queries to elicit specific responses from an original model, enabling detection of derivative models without requiring model knowledge or modifications, achieving a method that is non-invasive and applicable in real-world settings.
Large language models (LLMs) have attracted significant attention in recent years. Due to their "Large" nature, training LLMs from scratch consumes immense computational resources. Since several major players in the artificial intelligence (AI) field have open-sourced their original LLMs, an increasing number of individuals and smaller companies are able to build derivative LLMs based on these open-sourced models at much lower costs. However, this practice opens up possibilities for unauthorized use or reproduction that may not comply with licensing agreements, and fine-tuning can change the model's behavior, thus complicating the determination of model ownership. Current intellectual property (IP) protection schemes for LLMs are either designed for white-box settings or require additional modifications to the original model, which restricts their use in real-world settings. In this paper, we propose ProFLingo, a black-box fingerprinting-based IP protection scheme for LLMs. ProFLingo generates queries that elicit specific responses from an original model, thereby establishing unique fingerprints. Our scheme assesses the effectiveness of these queries on a suspect model to determine whether it has been derived from the original model. ProFLingo offers a non-invasive approach, which neither requires knowledge of the suspect model nor modifications to the base model or its training process. To the best of our knowledge, our method represents the first black-box fingerprinting technique for IP protection for LLMs. Our source code and generated queries are available at: https://github.com/hengvt/ProFLingo.