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We process the raw LateX files using this [script](https://github.com/togethercomputer/RedPajama-Data/blob/rp_v1/data_prep/arxiv/run_clean.py).
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This dataset has been used as source for 'member' documents to develop (document-level) MIAs against LLMs using data collected shortly before (member) and after (non-member) the training cutoff date for the target model ([the suite of OpenLLaMA models](https://huggingface.co/openlm-research/open_llama_7b)).
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For
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For non-members for the RDD setup, we refer to our [Github repo](https://github.com/computationalprivacy/mia_llms_benchmark/tree/main/document_level).
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We process the raw LateX files using this [script](https://github.com/togethercomputer/RedPajama-Data/blob/rp_v1/data_prep/arxiv/run_clean.py).
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This dataset has been used as source for 'member' documents to develop (document-level) MIAs against LLMs using data collected shortly before (member) and after (non-member) the training cutoff date for the target model ([the suite of OpenLLaMA models](https://huggingface.co/openlm-research/open_llama_7b)).
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For non-members for the RDD setup, we refer to our [Github repo](https://github.com/computationalprivacy/mia_llms_benchmark/tree/main/document_level).
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For more details and results see the section of Regression Discontiuity Design (RDD) in the paper ["SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It)"](https://arxiv.org/pdf/2406.17975).
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