Summary of Protecting Copyrighted Material with Unique Identifiers in Large Language Model Training, by Shuai Zhao et al.
Protecting Copyrighted Material with Unique Identifiers in Large Language Model Training
by Shuai Zhao, Linchao Zhu, Ruijie Quan, Yi Yang
First submitted to arxiv on: 23 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Cryptography and Security (cs.CR); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to membership inference for large language models (LLMs), addressing concerns about the training of these models on copyrighted online text. The authors suggest using unique identifiers, such as “ghost sentences,” which are passphrases made up of random words, to detect LLMs’ reliance on copyrighted content. The proposed methodology, called insert-and-detection, allows users and platforms to create their own identifiers, embed them in copyrighted text, and independently verify membership using two tests: perplexity test and last-k words test. The paper presents initial results for the perplexity test on LLaMA-13B and the last-k words test with OpenLLaMA-3B. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding out if big language models are using copyrighted text to learn from. Some people worry that these models might be copying text without permission, which could be a problem. The authors suggest a new way to check if a model has been trained on copyrighted content. They propose using special strings of words called “ghost sentences” and two tests to see if the model is using this information. This approach allows anyone to create their own unique identifiers, embed them in text, and then use simple tests to see if the model recognizes these identifiers. The paper shows that this method can work effectively for detecting membership inference. |
Keywords
* Artificial intelligence * Inference * Llama * Perplexity
