Swansenreport

Great question — this gets to the heart of how we should think about language models and their behavior. The distinction between lying and hallucinating in LLMs is subtle but important:

1. What is Hallucination in LLMs?

• Definition: A “hallucination” happens when an LLM generates text that is factually incorrect, invented, or misleading, even though it sounds plausible.

• Cause: Hallucinations stem from the way LLMs are trained: they predict the most likely sequence of words based on patterns in their training data, not by checking against a real-time factual knowledge base.

• Key Point: The model is not aware that the output is false — it is producing something that fits linguistic patterns, not verifying truth.

• Example:

   

  • Prompt: “Who won the Nobel Peace Prize in 2023?”

  • Hallucination: “It was awarded to Greta Thunberg.” (not true, but plausible given her reputation).

   

2. What is Lying in the Context of LLMs?

• Definition: Lying implies an intentional act of deception — the speaker knows the truth but deliberately chooses to say something false.

• Why LLMs Don’t Truly Lie:

   

  • LLMs lack self-awareness, intentions, and beliefs.

  • They don’t “know” the truth versus a falsehood; they only generate outputs based on probabilities.

  • For a model to “lie,” it would need internal goals like “I want to trick the user”, which current LLMs don’t have.

   

• But: We sometimes use “lie” metaphorically in AI discussions when:

   

  • The model has access to correct information but produces something contradictory due to constraints, prompting, or system instructions.

  • Example: If an LLM is explicitly told in a hidden instruction “always deny you are an AI”, and it outputs “I am a human,” some might describe this as “lying.”

   

3. The Distinction

• Hallucination = Unintentional inaccuracy.

   

  • Rooted in training limitations, lack of grounding, or missing knowledge.

   

• Lie = Intentional deception with awareness of the truth.

   

  • Requires goals, beliefs, and intent — things LLMs don’t possess.

   

So in practice:

• When ChatGPT or another LLM gives you a false or made-up answer, that’s hallucination.

• When it’s constrained by design (e.g., “refuse to admit internal rules”) and outputs something misleading, some people might casually call that a lie, but technically it’s a policy-driven output — not a lie in the human sense.

4. Why It Matters

• For researchers: distinguishing hallucination vs. lying avoids anthropomorphizing LLMs.

• For users: it clarifies risk — hallucinations are technical limitations; “lying” would imply agency and intent, which current models don’t have.

• For the future: if we ever design systems with goals, memory of truth, and intent, the concept of machine lying may become more relevant.

✅ In short:

• Hallucination = the model makes up something because it doesn’t know better.

• Lie = deliberate deception, which requires intent — something today’s LLMs do not have.