The predictor ablation reports that LLM-judge labels improve accuracy by 8.9 points over string-match labels without passages, and adding retrieved passages improves accuracy by a further 15.8 points.

The predictor ablation reports that LLM-judge labels improve accuracy by 8.9 points over string-match labels without passages, and adding retrieved passages improves accuracy by a further 15.8 points. · CiteArk

Measurement results

0/8 assessed

0 supported0 disputed or conflicting8 not assessed

The complete result group stays with its claim. Assessed rows come first; their judgments do not replace the whole-claim conclusion. Expand a row for conditions, sources, and evidence history.

Each row is a measurement with its reported value, latest available observation, and cumulative evidence state.
Metric and conditions	Reported	Latest observation	Evidence judgment
labels: string match · passages: excluded	54.7%	—	Not assessed
labels: string match · passages: excluded	0.547 score	—	Not assessed
labels: LLM judge · passages: excluded	63.6%	—	Not assessed
labels: LLM judge · passages: excluded	0.615 score	—	Not assessed
labels: LLM judge · passages: included	79.4%	—	Not assessed
labels: LLM judge · passages: included	0.785 score	—	Not assessed
	8.9 percentage points	—	Not assessed
	15.8 percentage points	—	Not assessed

The predictor ablation reports that LLM-judge labels improve accuracy by 8.9 points over string-match labels without passages, and adding retrieved passages improves accuracy by a further 15.8 points.

Reported and observed measurements

Assessments (0)

Runs (0)

Measurement results