Ward: Provable RAG Dataset Inference via LLM Watermarks
Nikola Jovanovi\'c, Robin Staab, Maximilian Baader, Martin Vechev
TL;DR
This paper introduces Ward, a watermark-based method for detecting unauthorized use of external datasets in RAG systems, providing rigorous guarantees and outperforming baselines in accuracy and efficiency.
Contribution
The paper formalizes RAG Dataset Inference (RAG-DI), creates a benchmark dataset, and proposes Ward, a watermark-based detection method with strong statistical guarantees.
Findings
Ward outperforms baselines in accuracy and robustness
Ward achieves higher query efficiency
The dataset enables realistic benchmarking of RAG-DI methods
Abstract
RAG enables LLMs to easily incorporate external data, raising concerns for data owners regarding unauthorized usage of their content. The challenge of detecting such unauthorized usage remains underexplored, with datasets and methods from adjacent fields being ill-suited for its study. We take several steps to bridge this gap. First, we formalize this problem as (black-box) RAG Dataset Inference (RAG-DI). We then introduce a novel dataset designed for realistic benchmarking of RAG-DI methods, alongside a set of baselines. Finally, we propose Ward, a method for RAG-DI based on LLM watermarks that equips data owners with rigorous statistical guarantees regarding their dataset's misuse in RAG corpora. Ward consistently outperforms all baselines, achieving higher accuracy, superior query efficiency and robustness. Our work provides a foundation for future studies of RAG-DI and highlights…
Peer Reviews
Decision·ICLR 2025 Poster
* I believe the paper studies an important and timely research question that has been overlooked in the literature. I am not a domain expert (in the sense of MIA and RAG privacy), so I may not know related work very well. * I think the paper has made many meaningful contributions, from formulating the problem to collecting the dataset and adapting RAG-MIA baselines, to finally proposing their own approach based on LLM watermarking. The experiments (different LLMs and evaluation settings) and an
The major part I am not very sure about is whether the dataset construction and experimental settings can really reflect realistic RAG settings in real-world deployment, since the effectiveness of the baselines and proposed approach all depends on this setting. In particular: * It assumes perfect retrieval (= only articles from the same source are used) in most experiments. Although the paper discusses a more practical retrieval setting in Section 5.3, I don't see any discussion of previous base
The strengths of this paper are mainly centered around the propose of the new task along with a new specialized benchmark dataset. 1. Novel Problem Definition and Formalization: The paper identifies and formalizes the novel problem of RAG Dataset Inference (RAG-DI), addressing a critical need for data owners to detect unauthorized data usage in RAG systems. This formalization fills a significant research gap and sets the stage for further exploration of secure data usage in RAG contexts. 2. In
Although this paper has the above strengths that are interesting, I also noticed several weaknesses that should be further considered. 1. Application Scenario. The authors propose a novel problem definition, namely RAG Dataset Inference (RAG-DI). The concept is quite straightforward, and one has to admit that the concern indeed exists in practice. However, it is still less discussed in this paper, that how often this type of problem can occur in realistic scenarios. I am concerned with the appl
1. The RAG - DI problem is formally defined, filling the research gap in this field and laying a foundation for subsequent research. 2.The design of the FARAD dataset takes into account the practical application scenarios of the RAG system and avoids the shortcomings of existing datasets in RAG - DI research, such as the possibility of being used for LLM training and the lack of fact redundancy. 3.The WARD method is based on LLM watermarks and can provide data owners with strict statistical guar
1. In real-world applications, RAG systems may face more complex situations, such as multilingual environments and data from different domains. The discussion in this regard in the paper is relatively limited. 2. There is a lack of discussion on the efficiency of the solution.
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Taxonomy
TopicsMachine Learning in Healthcare · Topic Modeling · Reservoir Engineering and Simulation Methods
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Attention Is All You Need · Sparse Evolutionary Training · WordPiece · Attention Dropout · Linear Layer · Weight Decay · Linear Warmup With Linear Decay · Dropout · Byte Pair Encoding