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To test an LLM rewrite, check whether readers can recover the source’s important facts and relationships from the rewritten text. Build questions from the original, answer them using only the rewrite, then separately look for unsupported additions and contradictions. Similarity scores can help screen outputs, but none proves that two texts mean the same thing.
What does it mean to preserve meaning?
A rewrite preserves meaning when it retains the important propositions in the source and the relationships among them—not merely when it uses similar words. It should keep material details such as who did what, to whom or what, under which conditions, and with what quantity, degree, or uncertainty. Negation, dates, comparisons, causal links, and caveats can all change a claim if they are dropped or altered.
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A fluent rewrite can still omit a key fact, strengthen a qualified claim, reverse a relationship, or introduce something the source never said. Lexical overlap and embedding-based similarity are therefore useful signals, not semantic proof.
A practical workflow for checking a rewrite
The steps below are a recommended working procedure, not a universal validated scoring standard. Keep the source and rewrite together throughout the check.
#1 Best Overall
1. Set the scope
Decide whether you are checking a sentence, paragraph, or full document. Include surrounding context when interpretation depends on it: an isolated sentence may not reveal a referent, condition, or fact established elsewhere in the text.
2. List the source facts that must survive
Before assessing the rewrite, make a compact checklist from the original. Capture the facts that matter to its purpose: actors and actions, affected entities, conditions, quantities, uncertainty, negation, dates, comparisons, and causal or temporal relationships. This is a practical way to make the evaluation concrete, rather than a checklist validated as a standard by the studies cited here.
3. Turn the checklist into questions
Write questions whose answers are explicit in the source. Then give a reviewer only the rewrite and ask them to answer each question, including an option such as “not answerable from this rewrite.” That option prevents a reviewer from filling gaps with guesses. Compare each response with the source-supported answer and record whether the rewrite preserved, weakened, strengthened, reversed, or omitted the point.
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This reader-comprehension approach is used in the human evaluation framework by Sweta Agrawal and Marine Carpuat, who ask whether readers can answer questions about key facts in simplified text using the original as the source of those facts. In their 2024 study, at least 14% of questions were marked unanswerable even for the best-performing supervised simplification system they evaluated. That result illustrates a risk in their text-simplification dataset and evaluation; it is not a failure rate for every LLM rewrite.
4. Look for additions and contradictions separately
Questions about source facts help identify information that has gone missing, but may not catch every invented detail. Inspect the rewrite for statements unsupported by the source and for statements that conflict with it. For consequential material, have a human review those claims instead of treating one model score as the final decision.
5. Use automated checks as a second view
Automated methods can help scale triage, but different methods look for different signals and can fail in different ways. Compare their output with a small, representative sample reviewed by people.
Rank #4
| Approach | What it checks | Useful role | Important limitation |
|---|---|---|---|
| Human source-based questions | Whether a reader can recover source facts from the rewrite | Main check when preserving information matters | Question design and reviewer time matter; results depend on the sample |
| Lexical overlap or semantic similarity | Shared words or learned similarity between texts | Fast screening and broad output comparisons | Similarity is not correctness and can miss specific omissions or contradictions |
| Question-answering evaluation | Whether source-grounded questions are answerable from the rewrite | Scalable approximation of information recovery | Depends on question generation and the QA system’s behavior |
| Entailment or NLI evaluation | Whether text supports, contradicts, or is unrelated to a claim | Claim-level support screening | Can be sensitive to paraphrasing and context |
| LLM judge | A model-generated assessment of consistency or meaning | Flexible triage at scale | Alignment with human judgment remains imperfect; the judge can introduce confounds |
In Agrawal and Carpuat’s 2024 paragraph-level text-simplification system comparison, SARI correlated better with reading-comprehension-based adequacy rankings than BERTScore and BLEU. Treat that as a result for their setting, not evidence that SARI is best for every kind of rewrite. More generally, BLEU and BERTScore can help compare outputs, but neither on its own establishes that all important meaning survived.
Entailment systems also need scrutiny. Verma, Lal, Sinha, Van Durme, and Poliak reported that contemporary textual-entailment models changed predictions on 8–16% of paraphrased examples in their 2023 PaRT E evaluation. This is a result on that benchmark, not an error rate for every evaluator or rewrite task.
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A 2025 meta-evaluation by Huidrom, Lorandi, Mille, Thomson, and Belz assessed 29 methods against human semantic-consistency ratings. They found that even the best correlations from LLM-based methods lagged behind correlations seen in other text-generation tasks. The finding concerns their data-to-text evaluation and does not establish a universal ranking of evaluators.
6. Check the evaluator, too
If an automated evaluator will be used to approve rewrites, test it with examples where the wording changes but the meaning stays constant, as well as controlled edits that change a number, negation, entity, condition, or relationship. The PaRT E results make paraphrase sensitivity worth checking: a judge that mistakes harmless wording changes for meaning changes—or misses a meaning-changing edit—can mislead the review.
7. Report the errors, not just a score
Keep examples of omissions, unsupported additions, contradictions, and harmless wording changes. Report how many source facts were retained or lost, and which error types matter for the intended use. Do not present a threshold as universally safe unless it has been validated for the relevant text type, stakes, language, and reader population; the cited work does not establish a universal pass score.
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The evidence supports using reader comprehension and factual consistency as important checks, but it does not identify one best method for every text type or prove that any single metric establishes equivalence. Agrawal and Carpuat’s metric comparison concerns paragraph-level text simplification; Huidrom and colleagues’ 2025 meta-evaluation concerns semantic consistency in data-to-text generation. Apply findings with their task and evaluation setup attached.
Consistency under meaning-preserving changes is also a broader evaluation concern. Elazar and colleagues’ ParaRel resource contains 328 paraphrases across 38 relations and tests whether pretrained models behave consistently under meaning-preserving alternations. Their article reports poor consistency among the studied models, with results varying by relation. This is a finding about those models and relations, not a direct measurement of current LLM rewrite quality.
Quick Recap
Sources
- Agrawal and Carpuat, “Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading Comprehension,” Transactions of the Association for Computational Linguistics, 2024.
- Verma, Lal, Sinha, Van Durme, and Poliak, “Evaluating Paraphrastic Robustness in Textual Entailment Models,” ACL, 2023.
- Huidrom, Lorandi, Mille, Thomson, and Belz, “Assessing Semantic Consistency in Data-to-Text Generation: A Meta-Evaluation of Textual, Semantic and Model-Based Metrics,” INLG, 2025.
- Elazar and colleagues, “Measuring and Improving Consistency in Pretrained Language Models,” Transactions of the Association for Computational Linguistics.
- Google Research, “TRUE: Re-evaluating Factual Consistency Evaluation.”
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