Comment by chenglong-hn
7 hours ago
Vega was a high-level language in the past for human, but now they can be a bit too low-level for AI agents! AI agents have to write a lot of low-level params just to make charts looking good, and the result is that programs are hard to write reliably for AI agents.
Flint is a higher-level abstraction, with simpler much shorter spec, and the compiler derives low-level decisions so that charts are looking good.
So: flint lets agent write short program that achieving good looking charts that had to be done with lengthy program in the past.
I'm sorry, but as someone who creates data visualisation as a big part of my job, I wouldn't say the charts on the website look good. Most aren't awful either, but by no means are they an improvement over what I'd get by telling any coding agent to make a chart with Vega-Lite or Observable Plot, and probably worse than if I had some decent instructions/skills.
I don't quite get what the goal of this is other than abstracting away a little bit of the complexity at the expense of flexibility. To me, the promise of LLMs is the opposite, I can get flexibility and customisation without the cost of complexity.
Some 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.
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.
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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...
1: https://observablehq.com/@observablehq/plot-gallery
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