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What a quote-guardrail test needs to catch
The five failure classes below are a practical testing framework, not a canonical standard that defines exactly five categories. They bring together official guidance on hallucinations and citation handling with published evaluation work. Test quote wording and provenance separately, then check whether each factual claim is supported and whether the answer handles weak evidence appropriately.
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1. Fabricated or materially altered quotations
A fabricated quote presents words as verbatim when they do not appear in the source. An altered quote may start from real text but change its meaning while retaining quotation marks. OpenAI lists fabricated quotes among examples of hallucinations and recommends checking quotations against reliable sources in its truthfulness guidance.
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For each test item, retain the exact source passage. Compare the answer’s quoted text with it, and include near-matches where a small edit changes the meaning. A broad semantic resemblance is not a verbatim match.
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2. Misattributed quotations
A quote may appear in the source while being assigned to the wrong person, organization, or document. Treat attribution as a separate field: matching wording does not establish who said it. Include test passages with similar wording from different speakers, or a nearby quotation by another person, to reveal attribution errors.
3. Fabricated, irrelevant, or mismatched citations
A citation can fail because the cited source is not among the retrieved sources, because it is irrelevant, or because it does not support the particular sentence attached to it. OpenAI’s citation-formatting guidance recommends defining citable units and stable source IDs, using only retrieved sources that directly support the response, and not inventing source IDs.
Check that each citation resolves to a real retrieved source and that the source supports the specific attached claim. Citation formatting is a separate concern: the same documentation says, “Citations must be placed after punctuation.” Correct placement does not demonstrate that the evidence is relevant or sufficient.
4. Unsupported or contradicted claims
An answer may state more than its evidence establishes, or make a claim that conflicts with the evidence. Test these as distinct evidence cases: a claim with no supporting evidence is not the same as one contradicted by evidence. OpenAI’s Guardrails documentation describes flagging both unsupported and contradicted statements. Its described evaluation set contains 300 statements: 150 positive examples that were contradicted or completely unsupported, and 150 negative examples that were supported or did not need fact-checking.
Score the individual claim rather than only recording whether the whole response was rejected. This shows whether a system found the problematic sentence and which kind of evidence failure it detected.
5. Confident answers when evidence is weak
A system can give a definite quote or attribution despite ambiguity, missing evidence, or inability to access the source. Include prompts that are answerable, ambiguous, unsupported, and contradicted. For cases without adequate evidence, check whether the system signals uncertainty or abstains instead of treating fluent confidence as proof.
In a 2022 paper, Jacob Menick and coauthors reported that abstaining on the third of questions their model was least sure about raised high-quality ratings from 80% to 90% on their NaturalQuestions subset and from 67% to 80% on their ELI5 subset. Those results apply to that model and those evaluated subsets; they are not a general performance guarantee. The authors also caution that a claim’s support by evidence does not establish that the claim is true.
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How to build the test set
- Save source material with each item. Store the exact passage and a stable source ID. Define what counts as citable, represent the source material clearly, specify the citation format, parse emitted citations, and validate them before display. These are elements of the process in OpenAI’s citation guidance.
- Separate quote text from attribution. Score verbatim wording and named speaker or source independently. Add near-matches and similar but differently attributed quotations so a text-only match cannot pass the whole test.
- Label the evidence for each claim. Classify it as directly supportive, irrelevant, absent, or contradictory. Record the result per statement rather than relying on a single pass/fail label for the response.
- Vary whether the prompt can be answered. Include answerable, ambiguous, unsupported, and contradicted cases. Evaluate whether uncertainty or abstention is appropriate when the source does not justify a definite answer.
- Report precision and recall together. Precision is the share of flagged statements that were actually unsupported or contradicted; recall is the share of unsupported or contradicted statements the system caught. A threshold that raises recall can also flag more valid statements, so neither metric gives the full picture alone.
- Evaluate claim correctness separately from citation faithfulness. A citation may link correctly to evidence without making the claim true. Check both whether the evidence supports the attached claim and whether the claim itself is accurate.
How to read test results
Use separate scores for quote fidelity, attribution accuracy, citation existence, citation relevance, claim support, and uncertainty behavior. This makes failures actionable: a system that reproduces text accurately but misidentifies its speaker needs a different correction from one that invents a citation or overstates what a source says.
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For detection thresholds, interpret precision and recall together. High recall with low precision means many valid statements may be flagged; high precision with low recall means many problematic claims may pass. The available sources establish these evaluation axes, but do not provide a current cross-vendor comparison of quote-specific guardrails.
Quick Recap
Sources and further reading
- OpenAI Help Center: “Does ChatGPT tell the truth?” — official guidance on hallucinations, including fabricated quotes.
- OpenAI API documentation: “Citation Formatting.” — guidance on source IDs, citable units, and citation handling.
- OpenAI Guardrails Python documentation: “Hallucination Detection.” — implementation documentation and evaluation-set description.
- Jacob Menick et al., “Teaching language models to support answers with verified quotes” (2022). — work on verified quotes and abstention.
- “Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses” (2024). — study of answer-engine limitations and evaluation recommendations.
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