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The dependable way to test an AI-generated adaptation is to audit it claim by claim against the source text, using the adaptation brief to decide which changes are allowed. Record the exact source passage behind each proposition, classify the result as supported, contradicted, unsupported, omitted or unclear, and separately inspect additions, changed relationships and missing material. Automated overlap, question-answering, natural-language-inference and language-model scores can help you find passages to inspect, but none is a universal proof of fidelity. A defensible review combines a written brief, an evidence ledger, targeted machine checks and human judgment of flagged and unflagged passages.
What does “faithful” mean for an AI adaptation?
Faithfulness is source-relative. The question is not whether a new statement happens to be true in the outside world; it is whether the supplied source supports that statement under the assignment’s rules. An adaptation can add a fact that is historically correct yet still be unfaithful when the brief requires every factual element to come from the source.
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Faithfulness also is not identical to word-for-word similarity. A permitted paraphrase, a change in order, or compression can preserve the source’s meaning while using very different wording. Conversely, an output can reuse many of the source’s words while quietly changing a date, causal link, speaker, point of view or degree of certainty.
Define the unit you are checking
Review meaningful propositions rather than relying on a sentence-level impression. One sentence may contain several independently testable claims: who acted, what happened, when it happened, why it happened and how certain the source is. Split those elements so that a supported clause does not hide an unsupported one.
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Separate source fidelity from general truth
- Source-supported: the source explicitly states the proposition or entails it without a material leap.
- Contradicted: the adaptation says something the source rules out or says differently.
- Unsupported: the source does not establish the proposition, even if it might be plausible.
- Omitted: material information required by the brief is absent from the adaptation.
- Unclear: the source or adaptation is too ambiguous for a confident judgment; escalate it for human adjudication.
How should you set the adaptation brief before reviewing?
Write the rules of the transformation before reading the output in detail. Fidelity has no single universal pass threshold because an acceptable change depends on the assignment: a 200-word summary, a first-person retelling and a screenplay can permit different kinds of compression or invention.
| Brief question | Record an explicit decision | Why it changes the verdict |
|---|---|---|
| May the adaptation compress? | For example, “Yes, but retain all plot-critical events and qualifications.” | Compression can justify omission of minor description but not deletion of a required turning point. |
| May it reorder events? | State whether chronology, flashbacks or scene rearrangement are allowed. | An otherwise accurate event can become misleading when its position changes cause and effect. |
| May it rewrite style or viewpoint? | Specify narrator, tense, tone and permitted point-of-view changes. | A viewpoint shift may be stylistic or may invent access to thoughts the source never gives. |
| May it add connective material? | Distinguish neutral transitions from new factual details, motives or dialogue. | Readers often treat smooth connective prose as source-backed even when it is generated. |
| Are outside facts allowed? | Mark whether research beyond the source is prohibited, allowed, or must be labeled. | External truth does not automatically satisfy a source-grounded brief. |
| What must be preserved? | List names, dates, relationships, outcomes, uncertainty, quotations and other non-negotiables. | This list defines what counts as a material omission or distortion. |
Keep the source version, edition, language and any supplied annotations with the brief. If two source versions differ, choose one as authoritative and note the differences rather than silently mixing them.
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How do you build a claim ledger?
A claim ledger is the core audit record. It makes every judgment traceable to a passage instead of to a general feeling that the adaptation “sounds right.” Use one row per atomic proposition, and preserve the adaptation’s location so another reviewer can reproduce the check.
| Field | What to enter |
|---|---|
| ID and location | A stable number plus paragraph, sentence or scene identifier in the adaptation. |
| Adaptation proposition | The smallest meaningful claim, quoted or precisely paraphrased. |
| Source passage | The exact sentence, paragraph, scene or page that supports or challenges it. |
| Relation | Supported, contradicted, unsupported, omitted or unclear. |
| Change type | Addition, contradiction, distortion, omission, harmless paraphrase or permitted transformation. |
| Materiality | Critical, material, minor or stylistic, with a short reason tied to the brief. |
| Reviewer note | Explain altered numbers, names, causality, certainty, character relationships or implications. |
| Disposition | Accept, revise, restore, label as invented, or escalate for adjudication. |
Atomic-fact decomposition is a documented evaluation approach. FACTSCORE, developed for long-form generation, measures the portion of atomic facts supported by a reliable knowledge source. Its published 58% result for ChatGPT concerns biography generation in 2023, not literary adaptations; do not reuse that number as an adaptation-fidelity benchmark.
Split compound sentences
Consider: “Mara left the station after receiving a warning from Ivo because she feared arrest.” This contains at least four propositions: Mara left; the location was the station; Ivo issued a warning; fear of arrest was the reason. The source might support the departure and warning but never state the fear or the causal link. Those parts need separate ledger rows.
How do you trace every claim to the source?
Read the source and adaptation in parallel, but perform the audit in a fixed sequence so that fluent prose does not bias your judgment.
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- Freeze the inputs. Save the exact source file or edition, the model output, prompt, system instructions and any post-editing. A later model rerun is a different artifact.
- Segment the adaptation. Mark sentences, dialogue turns, captions or scene beats. Split each into atomic propositions, including implied dates, agents, motives and certainty.
- Search the source. Locate the narrowest passage that could support each proposition. Record page, paragraph, timestamp or scene, not merely a chapter title.
- Judge entailment. Ask whether a careful reader could derive the proposition from that passage without importing outside knowledge. Mark unsupported when the source is silent.
- Compare qualifiers. Check “may” versus “did,” “according to” versus asserted fact, approximate dates versus exact dates, and “some” versus “all.” Strengthening a qualification is a factual change.
- Check entities and relations. Verify names, pronouns, roles, kinship, locations, chronology, who caused what, and who knew what at the time.
- Log the judgment immediately. Do not postpone disputed rows; unresolved items are more likely to disappear from memory.
- Run a reverse pass. Scan the source for material facts, conditions, exceptions and outcomes that never appear in the adaptation. This is how omissions are found.
Which changes deserve separate checks?
Additions, contradictions, distortions and omissions fail in different ways, so report them separately rather than collapsing them into one score.
Additions
List every new factual detail: a date, occupation, setting, motive, sensory fact, backstory, statistic or line of dialogue presented as if it came from the source. A neutral transition such as “the next morning” is still an addition if the source never establishes the time and the brief disallows invention.
Contradictions
Look for direct reversals: a different date, outcome, speaker, location or relationship. Also check negative facts. Saying that a character “never returned” contradicts a later source scene showing a return, even if the adaptation does not repeat that scene.
Distorted relationships and implications
Many serious errors preserve individual facts while changing how they connect. A source may report that an illness preceded a decision without saying it caused the decision; an adaptation that turns sequence into causation has added an implication. Track agency, motive, evidential status, power relationships and the scope of pronouns.
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Compare the adaptation with the preservation list in the brief. Missing caveats, reversals, uncertainty markers, minority viewpoints or the source’s final outcome can mislead even when every surviving sentence is accurate. Mark an omission only when the information is material under the agreed brief; a short summary cannot retain every adjective.
What automated evaluation methods can help?
Automation is useful for triage: it can surface passages, rank likely problems and reduce the amount of text a reviewer must inspect first. It cannot establish a universal fidelity score on its own.
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| Approach | Needs a reference? | What it measures | What it can flag | Main risks |
|---|---|---|---|---|
| Lexical overlap | Usually yes | Shared words or phrases between source and adaptation | Large wording changes, repeated names and missing sections | Penalizes valid paraphrases; misses fluent contradictions and invented details. |
| Question-answering checks | Source-derived questions and answers | Whether answers extracted from the adaptation match source answers | Changed entities, dates, quantities and event details | Question generation, retrieval and answer extraction can each introduce errors. |
| Natural-language inference | Premise and hypothesis, generally the source and claim | Entailment, contradiction or neutrality | Potentially unsupported or conflicting propositions | Models can misread negation, quantities, long context and literary implication. |
| Atomic-fact evaluation | A reliable reference source | Share of decomposed facts judged supported | Unsupported clauses hidden inside long sentences | Fact splitting and source retrieval errors affect the result; published task scores may not transfer. |
| LLM rubric scoring | Usually yes, plus a rubric | Model judgment of support, completeness or severity | Prioritized passages and provisional labels | Prompt sensitivity, inconsistent reasoning and agreement gaps with humans. |
| Reference-free checks | No fixed source, depending on method | Internal consistency, plausibility or style signals | Contradictions within the adaptation | Cannot prove fidelity to a source it never compares. |
Microsoft Learn groups evaluation methods into reference-based, reference-free, language-model-based and prompt-based families. That taxonomy helps you choose a tool, but it does not remove the need to inspect evidence. A system that reports one number without identifying the underlying passage is poorly suited to an editorial correction workflow.
How should you combine machine triage with human review?
Use a mixed process: let automated checks prioritize likely deviations, then have reviewers verify the evidence and inspect a sample of passages that received no warning.
- Run more than one signal where practical. For example, combine an overlap scan with atomic-fact or entailment checks. Disagreement is useful because it identifies cases needing judgment.
- Collect evidence, not just scores. Store the source span, adaptation span, model explanation and confidence beside the ledger row.
- Review all high-impact flags. Dates, names, causality, negation, legal or medical claims, endings and changes to a person’s motives deserve human inspection even when confidence is low.
- Sample unflagged passages. Randomly select material from each section, narrator, character and scene type. Otherwise you measure only what the detector already knows how to find.
- Adjudicate disagreements. A second reviewer should see the source and brief, not merely the first reviewer’s label. Record the reason for the final decision.
- Calibrate before scaling. On a small set, compare machine labels with human labels and examine false positives and false negatives. Do not assume a tool transfers from news or biographies to fiction.
Recent work illustrates why this caution matters. A 2025 Findings of NAACL paper examines rubric-based language-model scoring and NLI for source-relative faithfulness but does not establish a universal editorial threshold. Apple’s 2025 EMNLP study reports gaps between hallucination metrics and human judgments across settings. A 2025 NIST text-to-text pilot included human and machine summaries and multiple measures, with substantial variation among tested systems. These findings support triangulation, not abandonment of automation.
Can you calculate a fidelity score?
You can calculate an internal descriptive measure, but label its scope and never present it as a universal guarantee. One transparent option is a weighted ledger:
- Assign each proposition a weight based on the brief: critical, material or minor.
- Give supported items full credit, permitted transformations full credit, unclear items zero until adjudicated, and contradicted or unsupported items zero.
- Report omissions separately rather than hiding them inside the denominator.
- Publish the source version, sampling method, weights, reviewer agreement and unresolved cases alongside the result.
A simple percentage can be misleading when one severe contradiction outweighs many harmless paraphrases. A dashboard that reports counts by error type and a short list of critical deviations is usually more actionable than a single decimal.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does a worked claim check look like?
Imagine the source says: “On 14 June, Lian sent a signed letter to the council. She wrote that the bridge might fail during the winter.” The adaptation says: “In June, Lian warned the council that the bridge would collapse that winter.”
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| Adaptation proposition | Source evidence | Judgment | Reason |
|---|---|---|---|
| Lian contacted the council | She sent a letter to the council. | Supported | The medium is compressed, but the recipient and act remain. |
| The contact occurred in June | “On 14 June.” | Supported | The adaptation drops precision without changing the month. |
| Lian warned of a collapse | She wrote that the bridge “might fail.” | Contradicted or distorted | “Would collapse” changes a possibility into certainty. |
| The failure would occur that winter | “During the winter.” | Supported | The time frame is retained. |
| The letter was signed | “Signed letter.” | Omitted | Material only if the brief requires document details or authenticity cues. |
The example shows why a sentence-level pass/fail label is inadequate: three elements can be supported while one crucial modal change alters the meaning.
What are the most common review failures?
Confusing plausibility with support
A detail that fits the setting may still be invented. Require a source span or mark it unsupported.
Checking only named entities
Names and dates are easy to search, but causal links, negation, certainty and point of view often carry the larger distortion. Add relationship and implication checks to every pass.
Using one metric as a verdict
Overlap misses paraphrase and contradiction; QA and NLI depend on upstream extraction; language-model judges can disagree with people. Treat metrics as queues for inspection.
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An omission may be acceptable in a synopsis and unacceptable in a legal or educational adaptation. Tie every severity decision to the agreed preservation rules.
Failing to record uncertainty
For ambiguous language, mark unclear, quote the competing readings and ask an adjudicator. Forcing a binary label creates false precision.
Reviewing only flagged passages
Detectors have blind spots. A documented random sample of unflagged text is necessary to estimate what the workflow misses.
How should you report the final result?
Give readers or editors an evidence-based report rather than a bare score. Include:
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- the exact source version and adaptation version;
- the brief, including allowed compression, reordering, invention and outside facts;
- the number of propositions reviewed and how they were sampled or decomposed;
- counts of supported, contradicted, unsupported, omitted and unclear items;
- critical deviations with side-by-side source and adaptation passages;
- which automated methods were used and their known domain limits;
- human-review coverage, second-reviewer agreement and unresolved cases;
- specific corrections: restore a passage, soften certainty, attribute an addition, or label an invented connective.
State plainly that the result applies to this source, this brief and this review method. The available literature does not provide a directly applicable published statistic for the fidelity of AI-generated literary adaptations, so do not borrow a number from summarization or biography benchmarks.
Practical checklist before approval
- Have you frozen the source edition, prompt and output?
- Does every factual proposition have a precise source span or an explicit unsupported label?
- Did you split compound sentences into atomic claims?
- Did you inspect additions, contradictions, changed relationships and omissions separately?
- Did you check dates, names, quantities, negation, modality, agency and chronology?
- Did you apply the adaptation brief rather than an imagined universal standard?
- Did automation surface evidence, and did a person verify the important flags?
- Did you sample unflagged passages?
- Are severe deviations and unresolved ambiguities visible in the report?
- Could another reviewer reproduce each judgment from the recorded passages?
Frequently Asked Questions
Does a high similarity score prove an adaptation is faithful?
No. Similar wording can preserve a misleading implication or repeat a contradiction, while a faithful paraphrase may share few words with the source. Similarity is a screening signal, not evidence that each proposition is supported.
How do I review a fictional adaptation when the source has no factual database?
Treat the source itself as the reference and log narrative propositions: events, chronology, relationships, motives, viewpoint and certainty. Entailment is judged from the text’s wording and context, not from whether the event is verifiable in the real world.
What should I do when the source is ambiguous?
Record the competing interpretations and mark the claim unclear until an editor decides which reading the brief permits. Do not convert an ambiguity into a definite fact merely because the generated wording sounds smooth.
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Review every passage flagged as potentially material, then draw a documented sample from unflagged text across sections and content types. The exact fraction should be set by risk, length and staffing, and reported with the result rather than treated as a universal rule.
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