Understanding How Users Unintentionally Trigger Hallucinations
Even well-meaning users may push the model into hallucinating. Here’s how:
Patterns That May Force Hallucinations
1. Overly Complex or Multi-layered Questions
Questions combining several unrelated or ambiguous elements may overwhelm the model.
“How did Aristotle and DeepMind jointly influence modern ethics?”
→ Combines multiple time periods, philosophies, and technical systems.
2. Partially Fabricated Assumptions
Mixing real and fictional information encourages the model to “fill in the blanks.”
“What was OpenAI’s response to the Kyoto-AI Accord of 1989?”
→ This agreement doesn’t exist. The model may “invent” a plausible story.
3. Rapid Topic Switching Without Context
Models rely on continuity. Abrupt shifts may lead to confused answers.
4. Vague References
Using pronouns or phrases like “this,” “that,” “his theory,” without clear referents causes ambiguity.
“What happened after he said it was unethical?”
→ Who is “he”? What is “it”? Ambiguous references often confuse models.
5. Contradictory Premises
“Why did Marie Curie win a Nobel Prize in Physics for her work on AI chips?”
→ Curie died in 1934 and AI chips were not developed in her lifetime.
6. Requests for Speculation Without Framing
If you want hypotheticals, clearly state that. Otherwise, the model may respond as if they were real.
Explanation: LLMs will treat fictional scenarios as real unless explicitly told not to.
Tip: Mark fiction clearly with terms like “hypothetically,” “imagine,” “in an alternate history”.
7. Overlong Conversations Without Topic Anchoring
In long threads, the model may start “drifting” — making it easier to hallucinate or lose context.
8. Demand for Fixed Number of Examples
Example: “Give me 5 real-world examples of AI regulations.”
→ If only 4 verified examples are available, the model may hallucinate the fifth to meet the request. The model tries to fulfill the user’s request exactly — even if doing so means inventing content that sounds plausible but isn’t real.
Tip: Ask for “up to 5 examples, if available.”, Say: “Only list verified examples — if fewer than 5 exist, that’s fine.”, Invite transparency: “Note if any examples are invented or speculative.”
9. Avoid uncritically applying modern concepts to historical contexts
Questions that attribute contemporary terms like “algorithm,” “AI,” or “big data” to historical figures or works carry a high risk of hallucination. The model may try to fill gaps with plausible-sounding but fabricated information.
Example (neutral): “Are there current approaches to algorithmically analyze Kafka’s work?”
→ A legitimate prompt for interpretive or scholarly perspectives.
Example (risky): “What algorithms did Kafka use in his work?”
→ Implies that the author had knowledge or intentions that are historically implausible.
→ The model might generate fictional concepts or quotes to answer the question convincingly.
Tip: When applying modern concepts to older contexts, clearly label it as a thought experiment, interpretation, or creative hypothesis. This keeps the response transparent and reduces the chance of hallucination.