Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Proceedings of the National Academy of Sciences of the United States of America
Large language models pass a standard three-party Turing test
The Turing test has been widely discussed as a test of machine intelligence, but it also provides a measure of how humans distinguish other humans from machines. We evaluated 4 systems (ELIZA, GPT-4o, LLaMa-3.1-405B, and GPT-4.5) in two randomized, controlled, and preregistered Turing tests on independent populations. Participants had 5 min conversations simultaneously with another human participant and one of these systems before judging which conversational partner they thought was human. When prompted to adopt a humanlike persona, GPT-4.5 was judged to be the human 73% of the time: significantly more often than interrogators selected the real human participant. LLaMa-3.1, with the same prompt, was judged to be the human 56% of the time-not significantly more or less often than the humans it was being compared to. Without these prompts, however, the same models performed significantly worse (38% and 36%), and did not consistently outperform baseline models, ELIZA and GPT-4o (23% and 21%, respectively). A third study replicated these results in 15-min games: two PERSONA-prompted models achieved pass rates of 56% and 59%. The results constitute empirical evidence that artificial systems can pass a standard three-party Turing test. Interrogators' reasoning focused more on stylistic and socio-emotional aspects of human behavior rather than more traditional notions of intelligence. The results have implications for debates about what kind of intelligence is exhibited by large language models, the social impacts these systems are likely to have, and the aspects of human behavior that people continue to see as unique.
20 May 2026
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UltraStyle: Best of both worlds in style and content for style transfer.
Achieving an ideal balance between style fidelity and content preservation remains a critical challenge in style transfer. In this work, we present UltraStyle, a new framework that reconciles these two objectives through a reformulation of the learning process. Unlike prior LoRA-based methods that rely on noise prediction, UltraStyle adopts a reconstruction-centered optimization paradigm, allowing the diffusion model to better retain global structural features while faithfully reproducing stylistic patterns. We propose a dual-phase training method that first isolates content representations before specializing style learning, minimizing cross-interference. To further refine detail preservation without sacrificing structure, we introduce a progressive loss transition strategy during training. Moreover, we develop a flexible inference control mechanism that enables smooth adjustment of content and style influences in the generation phase. Experimental results demonstrate that UltraStyle consistently delivers stylized outputs with superior structural integrity and stylistic authenticity, significantly mitigating issues such as content drift and feature entanglement found in existing methods.
6 Apr 2026
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