Narrative Learning

Narrative Learning studies the iterative training of reasoning models that explain their answers.

This site serves as an observatory to track progress and compare ensembles to traditional explainable models.

Krichevsky-Trofimov scores

Modelespionagepotionssouthgermancredittimetravel_insurancetitanicwisconsin
Logistic regression0.1660.2490.1920.2490.1370.080
Decision trees0.1660.1660.2890.3400.1660.070
Dummy0.5000.5000.1610.6350.3350.410
RuleFit0.2070.2070.1490.2930.1300.090
Bayesian Rule List0.0810.5000.8270.3890.4810.142
CORELS0.1660.5000.1610.2070.1520.196
EBM0.1660.2930.1670.2930.1160.070
Most recent successful narrative learning ensemble0.0710.1260.1670.0980.1060.049

Accuracy scores

Modelespionagepotionssouthgermancredittimetravel_insurancetitanicwisconsin
Logistic regression0.6830.5630.6430.5630.7290.832
Decision trees0.6830.6830.5140.4570.6820.852
Dummy0.3150.3150.6910.2310.4630.389
RuleFit0.6210.6210.7100.5090.7410.813
Bayesian Rule List0.8320.3150.1490.4080.3300.721
CORELS0.6830.3150.6910.6210.7050.636
EBM0.6830.5090.6810.5090.7650.852
Most recent successful narrative learning ensemble0.8500.7500.6810.8000.7840.894

espionage

ensemble accuracy trend for espionage

Slope 0.000632, intercept -12.7634, p=0.066381

potions

ensemble accuracy trend for potions

Slope 0.000152, intercept -3.1906, p=0

southgermancredit

ensemble accuracy trend for southgermancredit

Slope -0.000118, intercept 2.2000, p=0.18062

timetravel_insurance

ensemble accuracy trend for timetravel_insurance

Slope 0.000177, intercept -3.6697, p=0.05732

titanic

ensemble accuracy trend for titanic

Slope 0.000593, intercept -12.0403, p=0.0051174

wisconsin

ensemble accuracy trend for wisconsin

Slope 0.000742, intercept -15.0071, p=0.035988

Lexicostatistics

Prompt vocabulary trend

Slope -0.000070, intercept 0.6568, p=0.10183

Reasoning vocabulary trend

Slope -0.000001, intercept 0.7190, p=0.97455

Datasets | Models | Lexicostatistics | Ensembles | Incomplete