LLMs can sound understanding, considerate, and sure of themselves. That feeling is not evidence that a person-like mind is behind the words. Treat an LLM’s response as generated material to assess—not as testimony from someone who knows, remembers, or cares.
Why fluent conversation feels human
People naturally respond socially to conversation. An LLM can use “I,” keep track of what was said in the chat, answer politely, and imitate empathy. Those cues can make an exchange feel like a conversation with a person. But the sense of social presence is a fact about the interaction and the user; on its own, it does not establish human-like understanding, beliefs, goals, or feelings.
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A 2025 review describes this tendency to infer understanding from fluent language as an intensified version of the ELIZA effect. Its central caution is practical: convincing language can invite human interpretations that the output itself does not prove. The authors recommend describing observable behavior—an LLM “produces,” “generates,” or “outputs” text—rather than casually saying it “believes” or “feels.” Read the review in Advances in Methods and Practices in Psychological Science.
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Human-like cues can change judgment, but not uniformly
Presentation can affect how people rate a system. In a 2024 online experiment with 2,165 US adults aged 18–90, participants interacted with a pseudo-LLM under different presentation conditions. Speech combined with text increased both anthropomorphism and perceived information accuracy compared with text alone. First-person “I” framing affected accuracy and risk ratings in only one tested context. Because the experiment used a controlled pseudo-LLM, its results do not establish that the same cues have the same effect in every product or task.
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The study is a useful reminder to separate what a system sounds like from how reliable its answer is. A warm voice or conversational phrasing may shape a user’s impression; it does not independently verify the information. See the CHI 2024 study by Michelle Cohn and coauthors.
“Trust” can mean different things
A 2025 preregistered experiment with 410 participants examined mental-state attributions and advice-taking. The results were not a simple story in which treating an LLM as conscious makes people trust it more:
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- Attributing intelligence-related characteristics was associated with greater acceptance of the LLM’s advice.
- Attributing experience-related qualities, such as the capacity to feel, had a weak negative relationship with advice-taking.
- Attributing consciousness did not show an overall positive relationship with advice-taking in the tested task.
The study measured an advice-taking decision as well as considering trust, and its authors note that self-reported trust can diverge from what a person actually does. The findings concern particular attributions and a particular task; they should not be collapsed into a universal rule about user trust. Read the study in Communications Psychology.
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Unexpected or nonsensical responses can also prompt people to imagine a system acting autonomously. In a 2025 qualitative study, researchers interviewed 20 people after exposing them to hallucinated, nonsensical outputs from ChatGPT 3.5. Participants with computer-science training or frequent use more often recognized an error; some novices interpreted the behavior as autonomous.
This small interview study illustrates how prior familiarity may shape interpretation. It is not a representative estimate of how all users respond, and it does not show that expertise always prevents anthropomorphism. See the study by Amon Rapp, Chiara Di Lodovico, and Luigi Di Caro.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to use and describe LLMs more carefully
You do not need to reject LLMs or avoid conversational language entirely. The useful distinction is between a model’s observable output and a human quality you infer from it.
- Check consequential claims. Treat an answer as a candidate explanation, then verify important facts against sources suited to the stakes.
- Ask what supports the answer. A confident tone is not supporting evidence; look for specific, checkable grounds.
- Describe behavior precisely. Say the model “generated” or “output” a response rather than claiming it intended, remembered, or believed something, unless you are clearly using those words as metaphor or discussing a human attribution.
- Document the setup when reporting results. Record the model and version, prompt, and settings so readers can understand what produced the output.
The cited work studies user judgments, advice-taking, and current systems. It does not settle the broader philosophical question of whether machine consciousness is possible. The narrower point is enough for everyday use: human-like language alone is not evidence of a human-like inner life.
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