Protecting Copyrighted Material with Unique Identifiers in Large Language Model Training
Shuai Zhao, Linchao Zhu, Ruijie Quan, Yi Yang
TL;DR
This paper proposes a novel insert-and-detect method using unique identifiers called ghost sentences for reliable membership inference in LLM training data, addressing false positives and usability issues.
Contribution
It introduces ghost sentences and a user-friendly last-$k$ words test as practical tools for membership inference, enhancing accuracy and interpretability.
Findings
Ghost sentences enable effective membership inference in LLMs.
The last-$k$ words and perplexity tests improve detection accuracy with fewer repetitions.
Study shows applicability of ghost sentences in real-world scenarios.
Abstract
A primary concern regarding training large language models (LLMs) is whether they abuse copyrighted online text. With the increasing training data scale and the prevalence of LLMs in daily lives, two problems arise: \textbf{1)} false positive membership inference results misled by similar examples; \textbf{2)} membership inference methods are usually too complex for end users to understand and use. To address these issues, we propose an alternative \textit{insert-and-detect} methodology, advocating that web users and content platforms employ \textbf{\textit{unique identifiers}} for reliable and independent membership inference. Users and platforms can create their identifiers, embed them in copyrighted text, and independently detect them in future LLMs. As an initial demonstration, we introduce \textit{\textbf{ghost sentences}} and a user-friendly last- words test, allowing end users…
Peer Reviews
Decision·ICLR 2025 Conference Withdrawn Submission
This is definitely an important problem which is clearly motivated. I appreciated that the authors conducted detailed ablations analyzing the memorization of the ghost sentences and presented detailed results with repetition rates, model sizes, length and insertion position, etc. The suggested techniques are also simple and intuitive. I also liked that the paper proposes a method to get interpretable p values for both the user-friendly k-words test and the perplexity based text.
The paper has the following limitations: - In my view, the work lacks novelty considering the overlap with [1] and [2] which introduce multiple varieties of data watermarks (or copyright traps) into the text and detect membership using loss-based metrics. The more practical perplexity-based test is also similar to the one proposed in [1] which compares the loss-based metric of a watermark against the empirical distribution. I believe it would help to include a detailed comparison with [1] and
1. The proposed method introduces accessible detection techniques, allowing non-technical users to check if their content has been used for LLM training. 2. The study thoroughly examines factors like model size, training data scale, and repetition frequency, ensuring the robustness of the proposed approach across different LLM configurations.
1. Perplexity-based filtering is a common method for cleaning pre-training data. How might the proposed ghost sentences method be adapted to remain effective in the presence of perplexity-based data cleaning techniques commonly used in LLM training? 2. Could the authors provide a more detailed analysis of how different wordlist impact the effectiveness of ghost sentences? Additionally, what are the trade-offs of using the entire LLM vocabulary as the wordlist versus a smaller, curated list? 3. W
- Presents an innovative approach to copyright detection using ghost sentences, filling a niche not fully addressed in LLM training. - Provides extensive experimental validation with different model sizes, data scales, and insertion strategies.
- Writing needs improvement, including but not limited to introduction - The example in Figure 1 is not self-explanatory - notations are confusing. Section 3.1 an example is indicated by both $x_i$ (with subscript) and $x$ (without subscript). - Dependence on specific models and configurations, which might not generalize well across all LLM architectures. - Experiments were only performed on the LLaMA family. There are alternative open-source models such as Mistral. - If I understand correctly,
1. Detect training data in LLMs is important for copyright protection. 2. The proposed method is effective in detecting training data. 3. The experiments are extensive and solid.
1. Can the perplexity test be applied to close-source models? 2. This paper uses finetuning to inject the unique identifiers. However, the problem studied in this paper is for detecting training data in pretraining, right? Is it possible that the finding and conclusion obtained from finetuning experiments be different for pretraining? 3. What are existing data filtering methods for LLM pretraining data preprocessing? Is the proposed method robust to these data filtering methods? 4. Is it poss
- The authors motivate the last-k word test by the need for easy-to-use membership inference methods that can be deployed by average users. I agree that this is an important and often overlooked aspect that can stand in the way of a broad adoption of such methods. - Besides the ease of use, the method also comes with the advantage of not relying on output probabilities that are often not available for commercial models. - The authors do a good job at covering various settings in their experiment
- My main criticism of the paper is the lack of novelty. The authors cite Wei et al. [1], who propose a very similar approach of introducing random sequences of characters (instead of random sequences of words) into documents. They mention three disadvantages of [1] when compared to their own method: 1. The model trainer could filter out the random character sequences. 2. There is a risk of false positives due to the prevalence of such random sequences in training data. 3. Random character seque
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Taxonomy
TopicsTopic Modeling · Privacy-Preserving Technologies in Data · Digital and Cyber Forensics
MethodsLLaMA