A big surprise for me was to find the explainability cluster quite far from the causality one. But I guess it stems from a cultural facet: causality is mainly the purview of statistics (with Pearl at the helm) with a strong medical sciences focus; when explainability is more of a reaction to algorithm used in the industry (trees, GLMs, neural networks, etc.); which you deploy for performance, and only then you care about knowing the why.
[shameless plug]
I created a personalized newsletter for Arxiv (which I plan to expand to others) so you can receive the latest research papers on the topics. You can also filter by some keywords too (e.g. only give me LLM or RAG related papers).
From the DataMapPlot Docs [1]:
"While the interactive plots work well in-line in a notebook, it is often useful to be able to send plots to others. The save method allows you to save the plot to a single HTML file that will embed (compressed) copies of the data. You can then share the HTML as required."
[0] https://github.com/lmcinnes/datamapplot_examples/blob/master...
[1] https://datamapplot.readthedocs.io/en/latest/interactive_int...
I guess the pipeline was embedding documents with an LLM (or even plain old word2vec average over the abstract might do it), and then reducing that to 2 dimensions with a cosine similarity metric with the help of UMAP.
I have no idea about colors and local cluster naming though. Maybe that's handcrafted.
