Safeguarding Language Model Copyright: Introducing EmbMarker 

In the realm of cutting-edge language models, the emergence of Large Language Models (LLMs) like GPT-3 has transformed natural language understanding and generation. Capitalizing on their capabilities, these models are now available as an Embedding as a Service (EaaS), catering to various natural language processing tasks. However, this accessibility raises concerns about potential model extraction attacks that could compromise the intellectual property and copyright of these LLMs. This is where EmbMarker steps in as a pioneering solution to protect the copyright of LLMs within the context of EaaS. 

Traditional copyright protection methods often fall short when applied to EaaS, primarily because of the unique characteristics of the service. EmbMarker takes an ingenious approach, combining the concept of inheritable backdoors with embeddings. This synergy results in a sophisticated watermarking technique that effectively counters the risk of model extraction attacks. By embedding triggers within the provided embeddings, EmbMarker strikes a delicate balance between safeguarding the model’s integrity and maintaining the utility of embeddings for downstream tasks. 

EmbMarker’s methodology unfolds in three sequential steps, ensuring comprehensive protection: 

  1. Trigger Selection: The process begins with the careful selection of moderate-frequency words, which serve as triggers. These triggers are drawn from a general text corpus and play a pivotal role in the watermarking process. 
  1. Watermark Injection: The core of EmbMarker’s innovation lies in the insertion of a target embedding, which acts as the watermark. This insertion is done proportionally based on the number of triggers present within the text. This proportional approach ensures that the watermarking process maintains a balance between protection and utility. 
  1. Copyright Verification: EmbMarker’s effectiveness is put to the test through copyright verification. Suspicious EaaS APIs are queried using texts containing backdoor triggers. This enables the assessment of whether copyright infringement has taken place, thus providing a mechanism for detection. 

The results of EmbMarker’s application are impressive, showcasing its ability to uphold the accuracy and privacy of embeddings. In comparison to baseline methods, EmbMarker consistently outperforms, ensuring that accuracy remains uncompromised. Furthermore, even when subjected to similarity-invariant attacks, EmbMarker maintains the confidentiality of backdoor embeddings. This resilience underscores EmbMarker’s robustness and its potential significance in safeguarding LLM copyrights within the realm of EaaS. 

EmbMarker stands as a groundbreaking innovation, addressing a critical concern in the rapidly evolving landscape of language models. By seamlessly integrating inheritable backdoors with embeddings, EmbMarker offers a solution that prioritizes both security and practicality. Its capability to counteract model theft and ensure copyright protection is a notable step forward in safeguarding the intellectual property of language models within embedding services. As EaaS continues to shape the domain of natural language processing, EmbMarker’s contribution holds promise in shaping a secure and protected future for these transformative technologies. 

Read the full research here

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