Using this benchmark for independent human-vs-LLM evaluation architecture
#2
by DRV2ME - opened
Hello — I am building an independent architecture called AdJudge Guardrails for
human-calibrated multimodal AI evaluation and review routing.
I am using this dataset as an external evaluation basis for studying
human-versus-LLM creative-quality disagreement. My project does not claim
ownership of this dataset, its annotations, or the underlying ad assets.
The architecture focuses on controls around evaluation use: provenance-aware
retrieval, freshness and metric-verification checks, tenant-safe context
handling, human-review routing, event forensics, and human-approved
remediation planning.
I wanted to ask:
- Are there any recommended caveats or interpretation guidelines beyond the
dataset card when reporting human-versus-LLM agreement? - Is there a preferred citation or attribution format for downstream
evaluation-architecture work? - Are there known annotation, rubric, or sampling limitations that downstream
users should emphasize when interpreting results?
Thank you for making the benchmark available.