Concern 732 · Working draft · editorial review in progress

AI-generated citations in papers

AI-generated citation error occurs when a paper includes a reference that does not exist, contains incorrect details or does not support the claim attached to it.

Understanding the concern

A citation is supposed to let a reader trace a statement back to evidence. A language model can assemble an author, title and journal into a convincing-looking reference without retrieving a real paper. Even a real source may be cited for a result it never reported.

Research has documented fabricated and inaccurate references in tested chatbot outputs. The practical remedy is more than checking that a link opens: the author must read enough of the source to confirm the claim. A citation is like a sign pointing to a bridge; its neat appearance does not show that the bridge exists or reaches the intended destination.

An example

A literature review cites a real paper, but the paper's findings contradict the sentence it is used to support.

Illustrative unless identified as a documented case.

Words in plain English

AI
Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
Model
The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.

Evidence and its limits

The study concerns specified models and prompts at the time of testing. It does not provide a current universal fabrication rate.

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Broader frameworks and background

These resources provide context. They do not establish every claim in this concern.

A question to discuss

Has someone verified both that the reference exists and that it supports this particular statement?

Keep track of different views, what needs checking, and any next step in your own discussion notes.

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Principles for the conversation

Before you finish · reflect together

Virtues and principles to consider

Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.

Learning together

Learning together means treating experience as something a group can examine and improve. It values honest feedback over appearing successful.

Agree on a question, try something manageable, and compare what different people observed. Share failures as well as gains. Decide together what the evidence supports and what remains uncertain.

Ask together: What have we learned together that should guide the next step?

Humility

Humility means recognizing the limits of our knowledge and being willing to learn from others. Expertise can be valuable without answering every question.

State where evidence is weak, invite criticism, and listen to people with different experience. Leave room to revise a decision when new information appears.

Ask together: What might we be missing, and who could help us see it?

Truthfulness

Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.

Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.

Ask together: What can we honestly say, and what still needs checking?

Editorial ratings

These are editorial judgments from the register, not measured probabilities or predictions about your situation.

Severity
Serious · 3/5
Likelihood
Widely observed · 4/5
Time horizon
Now
Register risk score
12
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More ways to explore

Search for more writing

For more specific or recent material, open a search below or ask for a web summary. AI-generated summaries can contain mistakes; open the original articles to check them.

Explore a proposed use of AI →