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--- |
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dataset_info: |
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features: |
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- name: meta |
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struct: |
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- name: arxiv_id |
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dtype: string |
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- name: language |
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dtype: string |
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- name: timestamp |
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dtype: string |
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- name: url |
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dtype: string |
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- name: yymm |
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dtype: string |
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- name: text |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 857168232 |
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num_examples: 13155 |
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download_size: 382068275 |
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dataset_size: 857168232 |
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--- |
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# ArXiv papers from RedPajama-Data originally published in February 2023 |
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We collect the ArXiv papers released shortly before the training data cutoff date for the [OpenLLaMA models](https://huggingface.co/openlm-research/open_llama_7b). |
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The OpenLLaMA models (V1) have been trained on [RedPajama data](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T). |
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The last batch of ArXiv papers included in this dataset are papers published in February 2023. |
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To get the members close to the cutoff data, we collect the 13,155 papers published in "2302" as part of the training dataset. |
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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). |
