Feedback Forensics is a tool to investigate pairwise feedback data used for AI training and evaluation: when used for training, what is the data teaching our models? When used for evaluation, towards what kind of models is the feedback leading us? Is this feedback asking for more lists or more ethically considerate responses? Feedback Forensics enables answering these kind of questions, building on the Inverse Constitutional AI (ICAI) pipeline to automatically detect and measure the implicit objectives of annotations. Feedback Forensics is an open-source Gradio app that can be used both online and locally.
See the GitHub repo for docs and details: https://github.com/rdnfn/feedback-forensics
Release files for feedback-forensics 0.5.0
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| feedback_forensics-0.5.0-py3-none-any.whl | Python 3 | none | any | Details for feedback_forensics-0.5.0-py3-none-any.whl |
Total release size: 16.7 MB
Release files / feedback_forensics-0.5.0.tar.gz
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