Groundedness, Hallucinations, and Citation Quality: What to Measure and What to Ship

“Groundedness” is one of those terms everyone uses differently. Users usually mean:

• “Is this answer based on my documents?”
• “Did the model make anything up?”
• “Can I prove it?”

One clear definition from a major provider: groundedness detection aims to ensure LLM responses are based on provided source material and to reduce fabricated outputs (“ungroundedness” is output not present in the sources).

So how do you actually build that into a product?

First: grounded ≠ correct

A grounded answer can still be wrong if:

• the source is wrong
• the source is outdated
• the system retrieved the wrong doc
• the extraction misread a number (bad scan, table issues)

So groundedness is necessary for trust, but not sufficient.

The 4 metrics that matter in real products

1) Statement-level support rate

Break the output into statements, then evaluate: is each statement supported by the cited sources?

This is the core idea behind automated citation support evaluation approaches.

2) Citation precision

“How often are citations attached to claims they don’t support?”

This catches “looks cited but isn’t” behavior.

3) Citation coverage (recall / comprehensiveness)

“How many important claims have no citation?”

A well-cited answer should not leave critical claims orphaned.

4) Human override rate

In document workflows, the strongest signal of groundedness quality is often:

“How often do humans edit or flag a value?”

This is also the easiest metric to compute once you have a verification UI.

What the research is warning you about

Even when systems provide citations, the citations may be post-hoc and not faithful—one study reports substantial post-rationalization and up to 57% unfaithful citations in experiments.

And in at least one large-scale health-related evaluation, many responses were not fully supported by cited sources (with some statements contradicted).

Your takeaway shouldn’t be “citations are bad.” It should be:

“Citations need verification and UX support. A ‘sources’ list isn’t a trust layer.”

Guardrails you can ship this sprint

Guardrail A: “No evidence, no answer” for critical fields

For document extraction, enforce that every accepted field must include:

• page
• snippet
• bbox (for highlight)
• confidence

CiteLLM returns these per field.

Guardrail B: Confidence threshold gating

Use confidence to decide: auto-approve vs verify vs manual.

CiteLLM supports options.confidence_threshold to filter low-confidence extractions.

Guardrail C: “Abstain” as a first-class outcome

When support is missing or conflicting, return:

• unknown
• needs_review
• conflict_detected

This is better than inventing an answer and citing something vaguely related.

A practical “groundedness score” you can implement

For a given output:

• split into claims (sentences or atomic statements)
• for each claim: check if at least one citation’s snippet supports it
• optionally run NLI/verifier

Score = supported_claims / total_claims

Then combine with:

• citation precision
• coverage
• override rate

This mirrors how modern evaluation approaches treat the answer as a set of verifiable units, rather than one blob of text.

Takeaway

When users search “groundedness score,” they’re asking for a number that predicts trust.

Give them more than a number:

• evidence objects
• click-to-verify UI
• confidence routing
• abstention when evidence is missing

That’s how groundedness becomes a workflow, not a buzzword.