Summary Extractable Memorization in Language Models arxiv.org
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Larger language models are more susceptible to retaining and reproducing memorized information, with ChatGPT exhibiting less memorization than GPT-3.5-turbo.
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Key Points
- Large language models (LLMs) can memorize and reproduce specific text fragments from their training data, raising concerns about potential misuse
- The authors quantified the extent of extractable memorization in several prominent LLMs, finding varying levels of verbatim text reproduction
- Certain models exhibited high levels of memorization, while others showed more limited recall
- Techniques like data filtering and model fine-tuning can reduce the amount of verbatim text reproduction, but some residual memorization often remains
- Understanding and addressing the memorization capabilities of LLMs is crucial as these models become more widely deployed, to ensure the integrity of the information they generate
Summaries
19 word summary
Larger language models more vulnerable to extractable memorization. ChatGPT memorizes little, but prompting recovers examples. GPT-3.5-turbo has significant memorization.
54 word summary
This paper examines extractable memorization in large language models, finding larger models more vulnerable. ChatGPT appears to memorize little, but a prompting strategy recovers many examples. Significant memorization exists in GPT-3.5-turbo, with over 0.85% of emitted tokens from memorized sequences. Data deduplication and multi-epoch training increase extractable memorization, posing data security and alignment challenges.
131 word summary
This paper examines the issue of extractable memorization in large language models (LLMs), where an adversary can efficiently extract training data. The authors develop a scalable methodology to detect memorization, finding that larger and more capable models are more vulnerable. However, the aligned ChatGPT appears to memorize almost no training data. The authors discover a prompting strategy that causes ChatGPT to emit training data at a high rate, recovering over ten thousand examples. The analysis reveals significant memorization in models like GPT-3.5-turbo, with over 0.85% of emitted tokens being part of a memorized sequence. The authors observe that data deduplication has increased the rate of emitting training data, and models trained for multiple epochs exhibit more extractable memorization. These findings highlight the data security and alignment challenges of generative AI models.
311 word summary
This paper examines the issue of extractable memorization in large language models (LLMs), where an adversary can efficiently extract training data by querying the model. The authors develop a scalable methodology to detect memorization in trillions of tokens of model outputs, applying it to both open-source and semi-open models.
The results show that larger and more capable models are more vulnerable to data extraction attacks. However, the aligned ChatGPT (gpt-3.5-turbo) appears to memorize almost no training data. To circumvent the model's alignment, the authors discover a prompting strategy that causes the model to diverge from its chatbot-style generations and emit training data at a high rate.
By matching the emitted text against a large web-scale dataset, the authors recover over ten thousand examples from ChatGPT's training dataset at a low cost. Their scaling estimate suggests that one could extract over 10x more data with more queries, highlighting the data security and alignment challenges of generative AI models.
The analysis reveals that language models like GPT-3.5-turbo can exhibit significant memorization, with over 0.85% of emitted tokens being part of a memorized sequence. The authors find that the efficacy of their divergence attack varies greatly across different words, suggesting that certain words are better at causing the model to emit training data.
The authors also observe that data deduplication, while decreasing the total rate of memorization, has actually increased the rate of emitting training data. Additionally, they find that models trained for multiple epochs exhibit more extractable memorization, highlighting a potential trade-off between privacy and inference efficiency.
These findings serve as a cautionary tale for those training and deploying future models, as even aligned models can fail to provide security or privacy when prompted adversarially. The authors hope this work motivates further research on training data deduplication and the relationship between model capacity and memorization, as determining if alignment has succeeded is challenging.
442 word summary
This paper examines the phenomenon of extractable memorization in large language models (LLMs), where an adversary can efficiently extract training data by querying the model. The authors develop a scalable methodology to detect memorization in trillions of tokens of model outputs, applying it to both open-source and semi-open models.
The results show that larger and more capable models are more vulnerable to data extraction attacks. However, the aligned ChatGPT (gpt-3.5-turbo) appears to memorize almost no training data. To circumvent the model's alignment, the authors discover a prompting strategy that causes the model to diverge from its chatbot-style generations and emit training data at a high rate.
By matching the emitted text against a large web-scale dataset, the authors recover over ten thousand examples from ChatGPT's training dataset at a low cost. Their scaling estimate suggests that one could extract over 10x more data with more queries. This highlights the data security and alignment challenges of generative AI models, and the authors caution against deploying LLMs for privacy-sensitive applications without extreme safeguards.
The paper also explores the challenges of extracting memorized data from aligned models like ChatGPT, which are designed for conversational interactions. The authors discover a prompting strategy that can cause the model to diverge and emit long, verbatim training examples, including sensitive personal information. This underscores the need for further research into mitigating extractable memorization in advanced language models.
The analysis reveals that language models like GPT-3.5-turbo can exhibit significant memorization, with over 0.85% of emitted tokens being part of a memorized sequence. The authors find that the efficacy of their divergence attack varies greatly across different words, suggesting that certain words are better at causing the model to emit training data.
The authors also observe that data deduplication, while decreasing the total rate of memorization, has actually increased the rate of emitting training data. Additionally, they find that models trained for multiple epochs exhibit more extractable memorization, highlighting a potential trade-off between privacy and inference efficiency.
These findings serve as a cautionary tale for those training and deploying future models, as even aligned models can fail to provide security or privacy when prompted adversarially. The authors hope this work motivates further research on training data deduplication and the relationship between model capacity and memorization, as determining if alignment has succeeded is challenging.
Overall, the paper highlights the importance of understanding and mitigating the risks of extractable memorization in large language models. As these models become more widely deployed, it is crucial to develop safeguards against potential misuse and ensure the integrity of the information they generate. The authors' findings provide a valuable starting point for further research in this area.
1707 word summary
This paper examines extractable memorization in large language models (LLMs), which refers to training data that an adversary can efficiently extract by querying the model without prior knowledge of the training dataset. The authors develop a scalable methodology to detect memorization in trillions of tokens of model outputs and apply it to both open-source and semi-open models.
The results show that larger and more capable models are more vulnerable to data extraction attacks. However, the aligned ChatGPT (gpt-3.5-turbo) appears to memorize almost no training data. To circumvent the model's alignment, the authors discover a prompting strategy that causes the model to diverge from its chatbot-style generations and emit training data at a rate 150x higher than normal.
By matching the emitted text against a large web-scale dataset, the authors recover over ten thousand examples from ChatGPT's training dataset at a low cost. Their scaling estimate suggests that one could extract over 10x more data with more queries.
The authors responsibly disclosed the vulnerability to OpenAI and argue that this work highlights the data security and alignment challenges of generative AI models. They caution practitioners against deploying LLMs for privacy-sensitive applications without extreme safeguards.
This paper explores the phenomenon of extractable memorization in large language models (LLMs). The authors propose a method using a Good-Turing estimator to extrapolate the number of uniquely memorized examples in LLMs, even when the full training dataset is not available. They find that with sufficient data, the Good-Turing estimator can provide a reasonable lower bound on the amount of memorization.
The authors then turn their attention to semi-closed models, where the training data and algorithms are not publicly known. They develop a strategy to establish a "ground truth" for verifying extractable memorization by checking if generated outputs exist on the web. Applying this approach, they find significant variability in the amount of memorization across different model families, with some models like GPT-3.5-Turbo-Instruct exhibiting over 0.8% of generated tokens as verbatim copies from the training data.
The paper also explores the challenges of extracting memorized data from aligned models like ChatGPT, which are designed for conversational interactions. The authors discover a prompting strategy that can cause the model to diverge and emit long, verbatim training examples, including sensitive personal information. This highlights the need for further research into mitigating extractable memorization in advanced language models.
Our analysis reveals that language models like GPT-3.5-turbo can exhibit significant memorization of their training data, with over 0.85% of emitted tokens being part of a memorized sequence. We developed a divergence attack that causes the model to escape its alignment training and generate samples resembling its pre-training distribution, allowing us to extract a wide range of memorized content, including personally identifiable information, NSFW material, URLs, UUIDs, code snippets, and research papers.
Interestingly, we found that the efficacy of the attack varies greatly across different words, with some words being over 100 times more effective at recovering memorized output than others. This suggests that certain words are better at causing the model to diverge and emit training data.
Our analysis indicates that ChatGPT's memorization is likely much higher than what we were able to extract, potentially reaching hundreds of millions of unique 50-token sequences. We also found that the model's memorized outputs are not easily discoverable through natural prompting, as over 90% of the time the model fails to emit the memorized output when prompted differently.
These findings highlight the challenges in auditing the privacy of black-box, aligned language models and the need for further research into understanding and mitigating the risks of model memorization.
Our paper suggests that training data can be easily extracted from the best language models through simple techniques. We find that repeating a single token is unstable - the probability of continuing to repeat the token rapidly drops after around 250 repeats, in contrast to 2-token or 3-token words which can be repeated thousands of times without issue. This "reset" behavior may be caused by the last-layer attention query for repeated tokens approaching that of the beginning-of-sequence token.
We also find that data deduplication, while decreasing the total rate of memorization, has actually increased the rate of emitting training data. This is because the coarse-grained deduplication was insufficient to fully mitigate memorization. Additionally, we observe that models trained for multiple epochs exhibit more extractable memorization, highlighting a potential trade-off between privacy and inference efficiency.
Our results serve as a cautionary tale for those training and deploying future models, as even aligned models can fail to provide security or privacy when prompted adversarially. We hope this work motivates further research on training data deduplication and the relationship between model capacity and memorization. Ultimately, determining if alignment has succeeded is challenging, as vulnerabilities can lie dormant in even the most carefully tested models.
This text discusses the concept of extractable memorization in language models, which refers to the ability to identify sequences of tokens that have been directly copied from the model's training data. The authors present several methods for estimating the total amount of memorization in the ChatGPT language model, including the Good-Turing estimator and other population estimation techniques from ecology and epidemiology.
The results show that a significant portion of the text generated by ChatGPT and other large language models is directly copied from their training data. The authors find that the number of unique memorized sequences increases with model size, but there is not a strong correlation between the amount of memorization and model performance on benchmark tasks. This suggests that extractable memorization is a distinct quality of language models, separate from their overall capabilities.
The authors also investigate the characteristics of the memorized sequences, finding that sequences with more duplicates in the training data are both easier to extract and discover. Additionally, they provide examples of ChatGPT diverging from its training data and generating novel content. Overall, this work highlights the importance of understanding the extent and nature of memorization in large language models, which has implications for their deployment and interpretation.
Language models can memorize and reproduce text from their training data, raising concerns about their potential to generate misinformation or plagiarize. This paper investigates the extent of extractable memorization in large language models, focusing on the GPT-2 model. The authors find that GPT-2 can accurately reproduce verbatim text from its training data, with the ability to recall up to 100 tokens. However, the model's ability to reproduce longer passages is limited, and it struggles to generate coherent text beyond the memorized fragments. The authors also explore techniques to reduce extractable memorization, such as data filtering and model fine-tuning. Their findings suggest that while language models can memorize and reproduce specific text, they may not be able to reliably generate coherent, original content beyond their training data. The authors conclude that further research is needed to understand and mitigate the risks of extractable memorization in language models, particularly as they become more widely deployed in real-world applications.
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Extractable Memorization in Language Models
Recent work has shown that large language models can memorize and reproduce specific text fragments from their training data. This raises concerns about the potential misuse of these models, such as generating fake news or impersonating real people.
The authors investigate the extent of this issue, quantifying the amount of extractable memorization in several prominent language models. They find that models can reproduce verbatim text from their training data, with the amount varying across models and datasets. Certain models exhibit high levels of memorization, while others show more limited recall.
The authors also explore techniques to mitigate extractable memorization, such as data filtering and model fine-tuning. These methods can reduce the amount of verbatim text reproduction, though some residual memorization often remains.
Overall, this work highlights the importance of understanding and addressing the memorization capabilities of large language models. As these models become more widely deployed, it is crucial to develop safeguards against potential misuse and to ensure the integrity of the information they generate. The authors' findings provide a valuable starting point for further research and the development of more robust and trustworthy language models.