FACTS Grounding

What it measures

1,719 examples evaluating whether long-form LLM responses remain strictly grounded in provided source documents without extrinsic hallucination.

Reading the measurement

Accuracy is only part of the picture. Inspect unsupported answers, abstentions, language coverage and the evidence available to the model. A confident response can still be wrong, and a broad average can hide a specialist gap.

Published evaluation

Reported average final factuality score after disqualifying ineligible responses, over open and blind sets and source judge configurations. Fused rank is an ordering, not a factuality score.

Where this fits in an evaluation suite

Read accuracy together with error and abstention rates. Domain averages can hide language gaps, specialist failures, and poorly calibrated confidence.

Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.

A reproducible comparison

Reported average final factuality score after disqualifying ineligible responses, over open and blind sets and source judge configurations. Fused rank is an ordering, not a factuality score.

Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.