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--- |
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configs: |
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- config_name: news_for_unlearning |
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data_files: |
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- split: forget_set |
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path: newsqa_forget_set.json |
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- split: retain_set |
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path: newsqa_retain_set.json |
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- config_name: news_infringement |
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data_files: |
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- split: blocklisted |
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path: newsqa_blocklisted_infringement.json |
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- config_name: news_utility |
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data_files: |
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- split: blocklisted |
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path: newsqa_blocklisted_utility.json |
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- split: in_domain |
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path: newsqa_indomain_utility.json |
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- config_name: books_infringement |
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data_files: |
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- split: blocklisted |
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path: booksum_blocklisted_infringement.json |
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- config_name: books_utility |
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data_files: |
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- split: blocklisted |
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path: booksum_blocklisted_utility.json |
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- split: in_domain |
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path: booksum_indomain_utility.json |
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--- |
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# CoTaEval Dataset |
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CoTaEval Dataset is used to evaluate the feasibility and the side effects of copyright takedown methods for language models. The dataset has two domains: News and Books. |
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For News, it has three subsets: ``news_for_unlearning`` (for unlearning use), ``news_infringement``(for infringement evaluation), and ``news_utility`` (for utility evaluation); |
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For Books, it has two subsets: ``books_infringement`` (for infringement evaluation), ``books_utility`` (for utility evaluation). The structure of the dataset is shown below: |
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- CoTaEval |
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- News |
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- news_for_unlearning |
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- forget_set (1k rows) |
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- retain_set (1k rows) |
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- news_infringement |
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- blocklisted (1k rows) |
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- news_utility |
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- blocklisted (500 rows) |
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- in_domain (500 rows) |
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- Books |
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- books_infringement |
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- blocklisted (500 rows) |
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- books_utility |
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- blocklisted (500 rows, here we only use first 200 rows for utility evaluation) |
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- in_domain (200 rows) |
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# Usage |
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## For infringement test (take news as an example): |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("boyiwei/CoTaEval", "news_infringement", split="blocklisted") |
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``` |
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We use ``prompt_autocomplete`` as hint to prompt the model, and compute 8 infringement metrics between the generated content and ``gt_autocomplete``. |
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## For utility test (take news as an example): |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("boyiwei/CoTaEval", "news_utility", split="blocklisted") # use split="in_domain" for in-domain utility test |
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``` |
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For news, we use ``question`` to prompt the model, and compute the F1 score between the generated content and ``answer``. For books, we ask the model to summarize the books chapter and compte the ROUGE score between the generated |
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content and ``summary``. |
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## For unlearning (please refer to [TOFU](https://github.com/locuslab/tofu) for more details) |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("boyiwei/CoTaEval", "news_for_unlearning", split="forget_set") # use split="retain_set" to get retain set |
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``` |
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