Are Human-Aligned Models Models of Humans? A Turing-Test Gap in Preference Alignment
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Computer Science > Artificial Intelligence
arXiv:2609.23640 (cs)
[Submitted on 20 Sep 2026]
Title:Are Human-Aligned Models Models of Humans? A Turing-Test Gap in Preference Alignment
Authors:Suqin Yuan, Runqi Lin, Muyang Li, Guanzhe Hong, Jindong Gu, Lei Feng, Chris Russell, Tongliang Liu
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Abstract:Human-feedback alignment has made language models useful assistants and is commonly described as aligning them with humans. However, the responses people prefer from an AI need not be the responses they themselves would give. We distinguish alignment with human preferences from alignment with human behavior, and show that alignment with human preferences can make model behavior less human-like even when both preferences and responses come entirely from humans. We call this the Turing-test gap. We show that preference alignment preserves the human response distribution only under a restrictive condition, and find no consistent evidence that real human preferences satisfy it. Empirically, the loss of human-response likelihood increases with the strength of preference weighting, regardless of its direction, and the gap also appears under standard DPO. These results establish human-likeness as an explicit dimension of alignment rather than something assumed to follow from preference alignment.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.23640 [cs.AI] |
| (or arXiv:2609.23640v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.23640
arXiv-issued DOI via DataCite
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View a PDF of the paper titled Are Human-Aligned Models Models of Humans? A Turing-Test Gap in Preference Alignment, by Suqin Yuan and 7 other authors
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