# Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models

arXiv: https://arxiv.org/abs/2310.15007 · HTML: https://mlsanity.com/paper/2310.15007/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- Placed in Membership inference attacks, one of 12 directions in LLM safety and security.
- Builds on 39 of its 73 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Placed in

- LLM safety and security / [Membership inference attacks](https://mlsanity.com/safety/membership-inference-attacks/)

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Solving Quantitative Reasoning Problems with Language Models](https://mlsanity.com/paper/2206.14858/) (2022)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Membership Inference Attacks From First Principles](https://mlsanity.com/paper/2112.03570/) (2021)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [Quantifying Memorization Across Neural Language Models](https://mlsanity.com/paper/2202.07646/) (2022)
- [Compressive Transformers for Long-Range Sequence Modelling](https://mlsanity.com/paper/1911.05507/) (2019)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [The Woman Worked as a Babysitter: On Biases in Language Generation](https://mlsanity.com/paper/1909.01326/) (2019)
- [Does Learning Require Memorization? A Short Tale about a Long Tail](https://mlsanity.com/paper/1906.05271/) (2019)
- [Large Language Models Can Be Strong Differentially Private Learners](https://mlsanity.com/paper/2110.05679/) (2021)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)

## Built on by (most cited first, 30 of 56)

- [Do Membership Inference Attacks Work on Large Language Models?](https://mlsanity.com/paper/2402.07841/) (2024)
- [Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models](https://mlsanity.com/paper/2404.02936/) (2024)
- [Blind Baselines Beat Membership Inference Attacks for Foundation Models](https://mlsanity.com/paper/2406.16201/) (2024)
- [SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It)](https://mlsanity.com/paper/2406.17975/) (2024)
- [Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data](https://mlsanity.com/paper/2409.19798/) (2024)
- [Copyright Traps for Large Language Models](https://mlsanity.com/paper/2402.09363/) (2024)
- [Generating Is Believing: Membership Inference Attacks against Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.19234/) (2024)
- [Data Extraction Attacks in Retrieval-Augmented Generation via Backdoors](https://mlsanity.com/paper/2411.01705/) (2024)
- [The Canary's Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text](https://mlsanity.com/paper/2502.14921/) (2025)
- [Membership Inference Attacks on Large-Scale Models: A Survey](https://mlsanity.com/paper/2503.19338/) (2025)
- [Position: Adversarial ML for LLMs Is Not Making Any Progress](https://mlsanity.com/paper/2502.02260/) (2025)
- [Detecting Training Data of Large Language Models via Expectation Maximization](https://mlsanity.com/paper/2410.07582/) (2024)
- [A Comprehensive Survey of Contamination Detection Methods in Large Language Models](https://mlsanity.com/paper/2404.00699/) (2024)
- [The Mosaic Memory of Large Language Models](https://mlsanity.com/paper/2405.15523/) (2024)
- [LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states](https://mlsanity.com/paper/2411.19876/) (2024)
- [SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks](https://mlsanity.com/paper/2506.10424/) (2025)
- [A Survey on Responsible Generative AI: What to Generate and What Not](https://mlsanity.com/paper/2404.05783/) (2024)
- [Copyright-Protected Language Generation via Adaptive Model Fusion](https://mlsanity.com/paper/2412.06619/) (2024)
- [Window-based Membership Inference Attacks Against Fine-tuned Large Language Models](https://mlsanity.com/paper/2601.02751/) (2026)
- [Cascading and Proxy Membership Inference Attacks](https://mlsanity.com/paper/2507.21412/) (2025)
- [Beyond Data Privacy: New Privacy Risks for Large Language Models](https://mlsanity.com/paper/2509.14278/) (2025)
- [Nob-MIAs: Non-biased Membership Inference Attacks Assessment on Large Language Models with Ex-Post Dataset Construction](https://mlsanity.com/paper/2408.05968/) (2024)
- [Leave No TRACE: Black-box Detection of Copyrighted Dataset Usage in Large Language Models via Watermarking](https://mlsanity.com/paper/2510.02962/) (2025)
- [Strong Copyright Protection for Language Models via Adaptive Model Fusion](https://mlsanity.com/paper/2407.20105/) (2024)
- [Unlearning Inversion Attacks for Graph Neural Networks](https://mlsanity.com/paper/2506.00808/) (2025)
- [What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests](https://mlsanity.com/paper/2507.11128/) (2025)
- [Black-Box Membership Inference Attack for LVLMs via Prior Knowledge-Calibrated Memory Probing](https://mlsanity.com/paper/2511.01952/) (2025)
- [Protecting Copyrighted Material with Unique Identifiers in Large Language Model Training](https://mlsanity.com/paper/2403.15740/) (2024)
- [CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage](https://mlsanity.com/paper/2507.16872/) (2025)
- [Membership Inference Attacks on Tokenizers of Large Language Models](https://mlsanity.com/paper/2510.05699/) (2025)

## Abstract (arXiv)

With large language models (LLMs) poised to become embedded in our daily lives, questions are starting to be raised about the data they learned from. These questions range from potential bias or misinformation LLMs could retain from their training data to questions of copyright and fair use of human-generated text. However, while these questions emerge, developers of the recent state-of-the-art LLMs become increasingly reluctant to disclose details on their training corpus. We here introduce the task of document-level membership inference for real-world LLMs, i.e. inferring whether the LLM has seen a given document during training or not. First, we propose a procedure for the development and evaluation of document-level membership inference for LLMs by leveraging commonly used data sources for training and the model release date. We then propose a practical, black-box method to predict document-level membership and instantiate it on OpenLLaMA-7B with both books and academic papers. We show our methodology to perform very well, reaching an AUC of 0.856 for books and 0.678 for papers. We then show our approach to outperform the sentence-level membership inference attacks used in the privacy literature for the document-level membership task. We further evaluate whether smaller models might be less sensitive to document-level inference and show OpenLLaMA-3B to be approximately as sensitive as OpenLLaMA-7B to our approach. Finally, we consider two mitigation strategies and find the AUC to slowly decrease when only partial documents are considered but to remain fairly high when the model precision is reduced. Taken together, our results show that accurate document-level membership can be inferred for LLMs, increasing the transparency of technology poised to change our lives.