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A Hugging Face blog post by Daniel Huynh describing an experiment assessing SelfCheckGPT NLI as a measure of LLM inconsistencies for predicting hallucinated outputs. It reports that scores were highly precise after a threshold and that a score of 0.8 corresponded to close to 80% of hallucinations being identified.
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Distill publishes articles about machine learning, including peer-reviewed explanations of neural network topics such as graph neural networks, feature visualization and feature-wise transformations. In a July 2, 2021 editorial, the team announced that after five years Distill would be taking a break.
Weka is open-source machine learning software issued under the GNU General Public License. It runs in Java and is presented as a workbench for data mining, with download, documentation, courses and FAQ resources and datasets available on the project site.
ScalaNLP is an umbrella project for machine learning and numerical computing libraries, including Breeze and Epic. Breeze covers linear algebra, numerical computing and optimization. Epic is a statistical parser and structured prediction library. The site also lists Puck, a GPU-powered parser built on grammars produced by the Berkeley Parser.
Magenta is an open source research project exploring the role of machine learning as a tool in the creative process, focused on art and music. It includes Magenta Studio, a collection of music plugins for Ableton Live, and Magenta RealTime 2, an open model and real-time neural synthesis engine.
Accord.NET is a .NET machine learning framework combined with audio and image processing libraries written in C#. It covers classification, regression, clustering, distributions, hypothesis tests and kernel methods, with imaging and signal processing components. The framework is intended for building computer vision, audition, signal processing and statistics applications, including commercial use. It offers sample applications, documentation and a wiki.
A Hugging Face blog post by Daniel Huynh describing an experiment assessing SelfCheckGPT NLI as a measure of LLM inconsistencies for predicting hallucinated outputs. It reports that scores were highly precise after a threshold and that a score of 0.8 corresponded to close to 80% of hallucinations being identified.
