Blabber: A Language for Chart Annotation

Blabber

📅 April 1, 2027✍️ Huichen Will Wang, Md. Rahat-Uz-Zaman, Md Dilshadur Rahman, Andrew M. McNutt, Paul Rosen, Alper Sarikaya, Chenglong Wang📚 CHI 2027 (submitted)🎯 article

@inproceedings{rahatuzzaman2027blabber,
  author    = {Huichen Will Wang and Md Rahat-uz-Zaman and Md Dilshadur Rahman and Andrew M. McNutt and Paul Rosen and Alper Sarikaya and Chenglong Wang},
  title     = {{Blabber}: A Language for Chart Annotation},
  booktitle = {Proceedings of the 2027 CHI Conference on Human Factors in Computing Systems},
  publisher = {{ACM}},
  year      = {2027},
  note      = {(Submitted)}
}

Annotations help readers link patterns and evidence to an author's message. However, annotation authors often have to express intentions through verbose and fragile specifications that hard-code low-level parameters such as coordinates, lengths, and text offsets. We present Blabber, a declarative annotation language that allows users to express high-level annotation intent through operators and data references, leaving low-level configuration to a geometry-aware compiler. The Blabber compiler models annotation layout as a constraint optimization problem to identify configurations that balance clarity and simplicity. We implement Blabber as a library supporting 18 chart types and 13 annotation operators. We present a gallery of Blabber charts and case studies comparing Blabber with annotation features in major visualization libraries. On an LLM-driven chart annotation authoring benchmark, AnnoBench, GPT-5.6 Sol achieves a mean quality score of 11.59/15 with Blabber, compared with 7.00/15 when directly outputting Vega-Lite code and 5.81/15 when using AnnoGram, a baseline annotation language.