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Potemkin Understanding in LLMs: New Study Reveals Flaws in AI Benchmarks
New research reveals that LLMs often fake understanding, passing benchmarks but failing to apply concepts or stay internally consistent.
https://voidful.github.io/NLPrep-Datasets/
Learn more from the docs.
pip install nlprep
nlprep --dataset clas_udicstm --outdir sentiment
You can also try nlprep in Google Colab:
$ nlprep
arguments:
--dataset which dataset to use
--outdir processed result output directory
optional arguments:
-h, --help show this help message and exit
--util data preprocessing utility, multiple utility are supported
--cachedir dir for caching raw dataset
--infile local dataset path
--report generate a html statistics report
Thanks for your interest.There are many ways to contribute to this project. Get started here.
Icons modify from Darius Dan from www.flaticon.com
Icons modify from Freepik from www.flaticon.com
FAQs
Download and pre-processing data for nlp tasks
We found that nlprep demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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