ICYMI, part 3 of our rubric series: The Science of Rubric Design. Iterate on rubrics like you iterate on models. Optimize objective alignment & expert agreement to make AI evaluations reliable & reproducible. Read → https://
snorkel.ai/blog/the-scien
ce-of-rubric-design/
… #AI #ML #RubricDesign #DataScience
@snorkelai
-
Science of Rubric Design for Reliable AI Evaluations
By
–
-
Complex Reasoning Error Modes in AI Model Evaluations
By
–
Highlights from recent evaluations (insurance underwriting & more): Surprising error modes in complex reasoning Trade-offs between tool use & efficiency Beyond accuracy: deeper evaluation with Snorkel Evaluate Full leaderboards →
-
Expert Network Delivers Reliable Scalable AI Evaluations
By
–
We apply these principles with our expert network to deliver reliable, scalable evaluations. Full post
-
Iterative Model Improvement Through Rubric Adjustment and Expert Evaluation
By
–
Iterative improvement: Freeze the model + environment while you tweak the rubric, run agreement evals + collect expert reviews to score, make number go up
-
Measuring LLM Judge Reliability and Human Alignment
By
–
Measure effectiveness: Quantitative inter-annotator & intra-rater reliability, human ⇄ LLM-judge alignment. Qualitative expert assessment for alignment with system objectives.
-
Rubrics as Models: Optimizing Stakeholder Alignment and Evaluator Agreement
By
–
Core idea: We can treat rubrics like models. Optimize along two axes: (1) Alignment with stakeholder objectives (2) Agreement among evaluators, both human and LLMAJ
-
Structuring AI Evaluation Rubrics with Hierarchical Granularity
By
–
Start by structuring the rubric to combine evaluation criteria distributed across a hierarchy of granularity. PaperBench provides great examples – S/O to @tejalpatwardhan and team @OpenAI for that paper:
-
AI Evaluation Rubrics Design Science Part Three
By
–
Part 3 of our AI evaluation rubrics series is live! The science of rubric design
-
Human Expertise and Iteration Drive AI Model Improvements
By
–
“Experts in the loop + tight iteration drove real gains for Dragon” (via Asha Sharma on @lennysan
). Very aligned with how we build at Snorkel. -
Data Quality Over Model Size: Enterprise AI Strategy
By
–
Bigger models ≠ better AI.
Better data = better AI. The future belongs to enterprises that can rapidly create, customize & evaluate their data. Snorkel co-founder Braden Hancock explains: https://
youtube.com/watch?v=27Dbq2
ykyko
… #AI #LLMs #DataCentricAI