How to Check for Hallucinations in AI Text

• AI hallucinations are confident-sounding statements, citations, or numbers that are not grounded in real sources, and they occur in every academic field.
• Researchers and students must verify every citation, quote, and statistic against a primary source before using AI-generated text in their work.
• ESL authors and first-time authors face a higher risk of missing hallucinations because fluent, well-structured prose can hide factual errors.

What Is a Hallucination in AI Text?

A hallucination is a statement, citation, or number that an AI tool generates with confidence but that has no basis in real facts or verifiable sources.

Some AI tools reduce hallucinations through grounding or retrieval-augmented generation, which lets the model pull information from real documents before writing a response. Even with these techniques, errors still occur, especially on niche topics, recent events, or subjects with limited training data. No current AI tool guarantees a hallucination-free output, so manual verification remains necessary for any academic or professional document.

Why Do AI Models Hallucinate?

AI models hallucinate because they are built to predict likely wording, not to retrieve verified facts, so when reliable information is missing they produce a confident guess instead of an admission of uncertainty.

Understanding the cause matters because each cause has a different fix. A hallucination that comes from a knowledge cutoff is solved by supplying the source. One that comes from a leading prompt is solved by rewriting the prompt.

Common Types of AI Hallucinations

Factual hallucination

The AI states something false as if it were established fact, such as the wrong founder of an organization or an incorrect date for a historical event.

Citation hallucination

The AI invents an author, title, journal, or page number, or attaches a real author’s name to a paper they never wrote.

Numerical hallucination

The AI reports a statistic, percentage, or measurement that was never published or that contradicts the original source.

Logical hallucination

The AI draws a conclusion that sounds reasonable but does not actually follow from the data or argument presented earlier in the text.

These 4 categories often overlap in a single document. A fabricated citation, for example, frequently supports a numerical hallucination, since the invented source is used to justify an invented statistic. Recognizing which type of error you are looking at helps you decide which verification method to use, whether that is a database search, a recalculation, or a rereading of the original argument.

Why authors often miss AI hallucinations

• A hallucination is generated with the same confidence, formatting, and tone as a real one, which is why simple proofreading is just not enough to catch hallucinations.
• A fabricated citation is usually perfectly formatted and even includes the name of a real journal in the field, at times.
• Readers slow down at awkward sentences and speed up at smooth ones, so the most fluent passages tend to receive the least scrutiny.
• This is the reason verification has to be a separate, deliberate pass rather than something done while reading the draft. Editage’s Post-AI Risk Assessment Services are designed to catch the most pressing AI hallucinations, through intensive checks conducted by human publication professionals.

Implications for Researchers and Students

Academic and professional writing depends on accuracy. A single fabricated citation or incorrect statistic can undermine an entire paper, delay publication, or damage a student’s academic record. Journals and universities increasingly run AI-detection and fact-checking software, so hallucinated content is more likely to be caught than in the past. One of the most popular preprint services, arXiv.org, recently imposed a one-year ban on authors who submitted papers with fabricated references.

Beyond detection risk, there is a research integrity issue. Citing a source that does not exist misleads readers who may try to locate it. Presenting an invented statistic as real can influence how other scholars interpret evidence. Checking for hallucinations protects both the author’s credibility and the wider body of knowledge that other researchers rely on.

There is also a time cost to consider. Correcting a hallucinated citation after a paper is under review is far more disruptive than catching it before submission. Reviewers and editors who find even 1 fabricated reference may question the reliability of the entire manuscript, which can slow down or end a review process that took months to reach.

How Can You Check AI-Generated Text for Hallucinations?

The table below breaks this process into 6 concrete steps. Follow them in order for any AI-assisted draft.

Step	What to Check	How to Check	Who Does This
1	Citations	Search the exact title and author in a library database or publisher site	Author/editor (though author must supply new citations in place of fabricated ones)
2	Quotes	Match the quote word for word against the original page	Author/editor (again, the author must find new quotes in place of fabricated ones)
3	Statistics	Trace the number back to the original dataset or analysis results	Author
4	Names and dates	Confirm spelling, titles, and dates against a reliable reference	Author or editor
5	Logic and flow	Check that conclusions actually follow from the evidence given	Author or editor
6	Tone and consistency	Check for style and terminology consistency	Editor

Free tools can support most of these steps. Google Scholar, CrossRef, and publisher websites help confirm whether a citation exists. A DOI lookup tool confirms whether a digital object identifier is real and points to the correct paper. For statistics, going back to the original dataset is the most reliable way to catch a numerical hallucination.

How Can You Prevent AI Hallucinations While Writing a Research Paper?

Break the Work Into Separate Tasks

• Run literature search, data analysis, drafting, and editing as separate steps, and verify the output of each one before using it as input for the next.
• Avoid chaining tasks in a single prompt, such as asking the AI to find sources and summarize them and draw conclusions all at once, since errors in an early step quietly carry into every step after it.
• Treat each output as a draft to check, not a finished product to paste into your paper.

Ground the AI in Real Sources

• Compile PDFs of the papers you have already selected and chosen to cite, and feed those directly to the AI tool instead of asking it to recall paper content from memory.
• When summarizing or analyzing a source, explicitly instruct the tool to use only the attached document, and spot-check the summary against the original text.
• Avoid asking an AI tool to “find papers on X,” since this relies on its internal recall of citations, which is where fabricated references are most likely to appear.

Avoid Generating Large Sections at Once

• Do not ask a tool to write an entire section or paper in 1 pass; generate a paragraph or subsection at a time so errors are easier to isolate and check.
• Write your own outline and key arguments first, and use AI mainly to help phrase, tighten, or restructure content you already understand, rather than to originate the content itself.
• Review each paragraph immediately after it is generated, rather than waiting until the full draft is finished, since errors are far easier to trace at the point they are introduced.

Know Your Own Data and Field

• Get familiar with your own data and analyses before asking AI for help, so you can immediately spot a number, trend, or result that feels off.
• Keep your own calculations, tables, or summary statistics open in a separate window while reviewing AI-generated text, so you can check claims against them in real time.
• Build enough background in your subfield to recognize a plausible-sounding but incorrect claim, since AI fluency can make a wrong statement read just as confidently as a correct one.

Additional Prevention Tips

• Use retrieval-augmented tools when available (e.g., Paperpal’s ChatPDF feature), since these ground responses in real documents rather than relying purely on the model’s memory.
• Ask the AI to quote directly from a provided source rather than paraphrase, since paraphrasing increases the risk of subtly altering a fact or number.
• Request that the AI flag uncertainty explicitly, and treat any unflagged claim with the same scrutiny, since models do not reliably self-report when they are guessing.
• Keep a running log of every AI-assisted step, including the exact prompt used, so any error can be traced back to where it entered your workflow.
• Cross-check any surprising or convenient-sounding result with a second, independent method or tool before including it in your paper.
• Set aside a fixed verification pass at the end, separate from drafting, dedicated only to checking citations, numbers, and quotes against original sources.

If you’re a first-time author or the paper is important for your career, consider Editage’s Post-AI Risk Assessment Services to get in-depth, human-led checks for key hallucinations and issues in AI-assisted manuscripts.

How to Spot Data Errors in AI Text

AI-assisted drafts can introduce numbers that drift from your actual dataset, especially when the same figure is repeated across sections. Use the workflow below to confirm every number in the paper before submission.

Step	Action	What to Check	Pass or Fail
1	Locate the source data	Trace every number back to the original dataset, spreadsheet, or output file	Traced or untraced
2	Recalculate key figures	Recompute at least 1 major statistic independently to confirm the method was applied correctly	Matches or mismatched
3	Check the abstract	Confirm every number in the abstract matches the corresponding number in the results, since summary sections are a common place for drift	Matches or mismatched
4	Check the methods section	Confirm sample sizes, variables, and procedures described match what was actually done	Matches or mismatched
5	Check the results section	Confirm every reported statistic, percentage, or test result matches your analysis output	Matches or mismatched
6	Check tables and figures	Confirm that numbers in tables and figures match the numbers stated in the surrounding text	Matches or mismatched
7	Check units and rounding	Confirm units are consistent throughout and rounding does not change the reported conclusion	Consistent or inconsistent
8	Check the discussion	Confirm that any number repeated or reinterpreted in the discussion still matches the original result	Matches or mismatched
9	Confirm internal consistency	Check that the same figure is not reported differently in 2 different sections	Consistent or inconsistent
10	Record the result	Log each figure as verified or corrected before the paper is finalized	Verified or corrected

A number should never be trusted simply because it appears consistently; consistency can mean the same error was copied across sections. Always trace figures back to the original data, not just to an earlier part of the same draft.

How to Check AI-Generated Code

Verify AI-generated code by running it against known test cases, checking every imported library and function actually exists, and reading the logic line by line instead of trusting that it compiles or runs without errors.

Researchers often treat working code as correct code. A script can run without crashing and still produce wrong results, use a deprecated method, or silently drop data. Code hallucinations are harder to catch than text hallucinations because the errors hide inside logic, not prose. Importantly, code hallucinations can only be caught by authors.

Common Types of Code Hallucinations

• Nonexistent packages: the AI imports a library or module that was never published, sometimes called “package hallucination,” which can even create a security risk if someone registers that fake package name with malicious code.
• Invented functions or parameters: the AI calls a method that does not exist in the actual library, or uses a real function with parameters that do not match its documentation.
• Silent logic errors: the code runs and produces output, but the underlying calculation, statistical test, or data transformation is wrong.
• Outdated syntax: the AI generates code for an old version of a language or library that behaves differently from the version the researcher is actually using.
• Fabricated benchmarks: the AI reports that a function or model achieves a certain speed or accuracy without this ever being tested.

A Verification Checklist for Researchers

Step	What to Check	How to Check
1	Every import	Confirm the package exists on the official registry (for example, PyPI or CRAN) and is actively maintained
2	Every function call	Compare it against the current official documentation, not the AI’s description of it
3	Output correctness	Test with a small, known dataset where you already know the correct answer
4	Edge cases	Run the code with empty, missing, or extreme values to see if it fails silently
5	Statistical logic	Recalculate 1 result by hand or with a trusted tool to confirm the method is applied correctly
6	Version compatibility	Check that the code matches the language and library versions used in your environment

What a Supervisor/Advisor/Experienced Colleague Can Check

• Whether the code follows standard style and structure for the language.
• Whether variable names and comments match what the code actually does.
• Whether the logic looks reasonable at a high level, based on their own experience.

What the Researcher Must Verify Personally

• That every package and function referenced actually exists and is being used correctly.
• That the output matches an independently calculated or previously published result.
• That randomness is controlled with a fixed seed, so results can be reproduced.
• That the code does exactly what the methods section of the paper claims it does.

• Whether the code is reproducible, meaning it runs the same way on a different machine.

 

Only the researcher who understands the underlying method can confirm that clean code is also correct code.

 

Frequently Asked Questions

 

Can AI Detection Tools Also Catch Factual Hallucinations?

No. Most AI detection tools identify writing style patterns, not factual accuracy. A hallucinated fact can pass an AI detector completely undetected, so fact-checking must be done separately.

What Percentage of AI-Generated Citations Are Typically Fake?

Rates vary by tool and topic, and no single fixed percentage applies across all studies. Some published tests have found fabricated citation rates ranging from under 10% to over 50%, which is why manual checking remains essential.

Can an AI Tool Be Listed as a Source in the Reference List?

If the AI output is a stable, shareable link that anyone can access and verify the exact prompts the author used, that AI output can be cited in the paper. APA, MLA, and other style guides have guidelines around this. But note that AI as a source is usually considered much weaker than peer-reviewed research. It’s much better to cite real journal articles, books, or conference papers wherever possible.

Why do AI tools hallucinate instead of saying they do not know?

Because language models generate the most likely continuation of a prompt, and confident text appears far more often in training data than admissions of uncertainty. Most models are not rewarded for abstaining, so a plausible guess becomes the default output whenever reliable information is missing.

Does ChatGPT hallucinate more than other AI models?

No tool is free of hallucination, and rates vary by model version, prompt wording, and subject area rather than by brand. Tools that read real documents before answering generally fabricate fewer citations than tools relying on internal memory, but every output still requires manual verification.

Can you stop ChatGPT from hallucinating by instructing it not to?

No. Instructions such as “only use real sources” or “do not invent anything” give the model no mechanism to check its own output against reality. Reliability improves only when you supply the source documents yourself and restrict the tool to those documents.

Which parts of a research paper are most at risk of hallucination?

Reference lists, literature review paragraphs summarizing papers the tool was never given, statistics repeated across the abstract and discussion, and any claim about very recent or highly specialized work.

Does retrieval-augmented generation eliminate hallucination?

No. Retrieval confirms that a document exists but does not confirm that the model interpreted it correctly. A grounded tool can cite a genuine paper and still attribute a claim to it that the paper does not make.