Excellent technical work but subject to the same
major questionmarks around the morality and legality of LLM business models. From the discussion section:
> Low Resource Grammars: ... LIDA depends on the underlying LLM s having some knowledge of visualization grammars as represented in text and code in its training dataset (e.g., examples of Altair, Vega, Vega-Lite, GGPLot, Matplotlib, represented in Github, Stackoverflow, etc.). For visualization grammars not well represented in these datasets
(e.g., tools like Tableau, PowerBI, etc., that have graphical user interfaces as opposed to code representations) ), the performance of LIDA may be limited without additional model fine-tuning or translation.
In other words, open source programmatic visualizations are required to feed the LLM, which then can, e.g., be licensed to corporates to accelerate various internal exploratory data analyses. A win-win for corporates and LLM providers.
Spot the loser.
> Low Resource Grammars: ... LIDA depends on the underlying LLM s having some knowledge of visualization grammars as represented in text and code in its training dataset (e.g., examples of Altair, Vega, Vega-Lite, GGPLot, Matplotlib, represented in Github, Stackoverflow, etc.). For visualization grammars not well represented in these datasets (e.g., tools like Tableau, PowerBI, etc., that have graphical user interfaces as opposed to code representations) ), the performance of LIDA may be limited without additional model fine-tuning or translation.
In other words, open source programmatic visualizations are required to feed the LLM, which then can, e.g., be licensed to corporates to accelerate various internal exploratory data analyses. A win-win for corporates and LLM providers.
Spot the loser.