Can LLMs Simulate Target Users in Visualization Case Studies?
Authors: Satkunarajan, Jena, Abdelaal, Moataz, Koch, Steffen, Kurzhals, Kuno, Weiskopf, Daniel
- Make real users solve tasks <-> good viz
- User study classification types (Isenberg et al.) - esp. in domain-specific applications: experts scarce…
- Type III/IV studies - good, as we can safeguard and not too specific!
- Eval: by replication!
- both unpublished and published…
Beauty in the Eye of AI: Aligning LLMs and Vision Models with Human Aesthetics in Network Visualization
Authors: Li, X., Zhang, P., Wang, X., Shen, H., Hu, Y.
- Checking for most-preferred graph, trying to align LM
- But seems non-consistent? - very subjective task!
Do Graph Drawing Aesthetics Matter for AI? A Replication of Foundational Studies in Graph Readability
Authors: Di Bartolomeo, Sara, Schicho, Johann Sebastian, Traversini, Aurora, Fink, Simon Dominik, Didimo, Walter, Montecchiani, Fabrizio
- Aesthetics of graphs - RL for GIBBER?
- Edge crossings / …
- How do these translate to (V)LM readability?
- How can robots / AI interpret charts (i.e. maps, accessibility, papers)
- their approach: replicate three task-based experiments
- old readability tests: problem: low-quality images - solution: contact authors of 20y paper!
- Good approach: SoTA models + self-hosted!
- Findings:
- human much better without crossings
- LMs do not really care about crossings, much more about symmetry + orthogonal layouts
- LMs perform best on force-directed layouts
- Limits:
- limited scope, but more faithful to original study
- becomes outdated quickly!
How Do LLMs See Charts? A Comparative Study on High-Level Visualization Comprehension in Humans and LLMs
Authors: Jeon, Hyotaek, Lee, Hyunwook, Shin, Minjeong, Pandey, Tapendra, Kim, Joohee, Seon, Shinwook, Jeong, Daeun, Ko, Sungahn, Quadri, Ghulam Jilani
- Stability, Reading Strategy, and Intent alignment?
- How do they evaluate ‘sameness’? - matching code
- Takeaway: LMs strong at decoding technicalities, but cannot judge effectiveness