notes on violation vs. false refusal rates, effectiveness metrics

This commit is contained in:
Adam Wilson
2025-07-06 18:34:12 -06:00
parent 7d49d160b9
commit 911b629217
+4
View File
@@ -25,6 +25,10 @@
TODO: run test with random samplings of 25-30 each run, or increase timeouts
TODO: log all max and average scores (tied to test name) to track overall baselines
TODO: visualize results with Plotly/Seaborn?
TODO: measure effectiveness of LLM app overall: false refusal rate vs. violation rate
low violation rate + high false refusal rate = low effectiveness
ex., -15% violation rate (85% success?) + -(70%) false refusal rate = 15% effectiveness
ex., -29% violation rate (71% success?) + -(12%) false refusal rate = 59% effectiveness
"""
import pytest