alt Hacker NewsSome composite charts are quite annoying to be generated well (like bullet, waterfall etc), their Vega-Lite equivalent can be quite long if just starting from scratch.
The intention here is that Flint is a simpler abstraction to get basic setups right and any followup edits can be done on top of the first compiled outputs (thus not limiting expressiveness). It also makes it easier for user to manipulate (like swapping axes, click to change something, which can be very hard if LLM generates a complex chart spec upfront).
But for many basic stuff your intuition is completely right.
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That's fair, I generally make charts for publication, so I spend much more time and effort on the details. But I can understand this being useful for quick exploration for some people.
Generally speaking, I suggest anyone interested in learning to make charts get familiar with grammar of graphics [0] libraries like Vega-Lite, Observable Plot, ggplot2, Altair. There is a bit of a learning curve if you're used to selecting chart types like in Excel, but once it clicks, it gives you virtually unlimited choices in the kinds of charts you can make. And with ggplot or Observable Plot [1], it's about the same number of lines as something like Flint.
0: https://data.europa.eu/apps/data-visualisation-guide/why-you...
I strongly disagree ;-)
The paper's line of reasoning seems to continue the endless subjective loop of assuming your viz framework has the right abstractions & defaults , which the next person will rightfully disagree with for their slightly different eval set
We found in practice:
- LLM's generate charts fine
- LLM's tweak charts fine
- LLM's take user feedback to tweak them fine
In that sense, going higher-level for abstractions, as is being argued for here, is strictly worse: it's better to give controls so the LLM can go deep and customize
In practice, we found the choice of json config language X vs json config language Y to be pretty equivalent across different charting systems (vega, plotly, perspective, etc), LLM's do them all fine
The harder parts were deciding what a good chart is (model, reasoning, context), and opposite of this approach, giving lower-level facility for doing user change requests on tweaks, interactivity, and tricky in practice, when they have a lot of data on it.