title: Cultural Riddles
emoji: 🧩
colorFrom: indigo
colorTo: blue
sdk: static
app_file: index.html
pinned: false
Cultural Riddles: results dashboard
Paper: CulturalRiddles: A Multicultural Benchmark of Riddles
The dashboard is one static HTML page with Plotly loaded from a CDN. It shows which model solved which riddle, the leaderboard with each model's spread across communities, a model-by-community grid, a page for each community, geography and language resources, how answers were scored, the controlled comparisons, and how the benchmark is built. Published pages need an internet connection for Plotly, but no Python runtime or application server. The front matter at the top of this file configures a static Hugging Face Space when you upload this README and the built page together.
Data and build contract
Run commands from the repository root, using the root uv environment:
uv run python -m analysis.export_dashboard
uv run python -m dashboard.build_static
analysis.export_dashboard uses the shared analysis loaders and table functions,
canonical inputs under data/canonical/, and topic summaries under
data/derived/. It writes aggregates only to
data/derived/dashboard/dashboard_data.json: no answers, riddle text, individual
survey responses, or contributor identities.
The export also carries fitted local-term odds ratios and confidence intervals
from the released riddle-feature summary. Translation comparison model labels
come from the shared English-condition configuration map; the high-resource
comparison is recomputed for those same configurations. These headline values
are rendered from JSON, not fixed in the page template.
The riddle matrix records, for riddles that every model has a score for, which models solved each one, as one bit per riddle and model. It carries each column's community and solve count, not riddle IDs or text.
dashboard.compute loads that JSON and validates relationships between the
leaderboard, score distributions, model-community grid, per-community topics,
riddle matrix, coverage, error types, topics, and paired comparisons. It does not pin historical numerical results.
dashboard.build_static uses only that JSON and Python's standard library; it
does not import the analysis pipeline or read its source tables. It embeds the
validated aggregates in template.html (layout, styles and chart code) and
writes the ignored build artifact dashboard/index.html.
You can rebuild from an existing JSON export without regenerating analysis.
To run the data consistency checks without writing HTML:
uv run python -m dashboard.compute
For prerequisites, deployment, and browser smoke checks, see Rebuild and publish the dashboard. There is no nested Python project or lockfile.