UC San Diego: Large Language Models Pass the Turing Test

UC San Diego: Large Language Models Pass the Turing Test

Summary of Read" class="text-blue-600 hover:text-blue-800" target="_blank" rel="noopener noreferrer">https://arxiv.org/pdf/2503.23674'>Read Full Report

Researchers Cameron R. Jones and Benjamin K. Bergen investigated whether advanced large language models (LLMs) can pass the standard three-party Turing test. Their study involved human interrogators conversing with both a human and an AI, then judging which was human.

The findings indicate that GPT-4.5, when prompted to adopt a persona, was identified as human significantly more often than the actual human participant, marking the first empirical evidence of an AI passing this rigorous version of the test.

While other models like LLaMa-3.1 showed some human-like qualities, only the persona-prompted models consistently fooled human judges. The study also explored the strategies used by interrogators and the implications of these results for our understanding of AI capabilities and their societal impact.

• This study provides the first empirical evidence that a Large Language Model (LLM), specifically GPT-4.5 when prompted to adopt a humanlike persona, can pass a standard three-party Turing test. In the study, GPT-4.5-PERSONA was judged as human 73% of the time, significantly more often than the actual human participants.
• Prompting with a humanlike persona was crucial for success. While GPT-4.5-PERSONA and LLAMA-PERSONA were not reliably distinguished from humans, the same models without the persona prompt (GPT-4.5-NO-PERSONA and LLAMA-NO-PERSONA) performed significantly worse, often being identified as AI at rates significantly below chance in the undergraduate study.
• The study compared the performance of several AI systems, including GPT-4.5, LLaMa-3.1-405B, GPT-4o, and ELIZA. The baseline models, GPT-4o-NO-PERSONA and ELIZA, had significantly lower win rates, indicating that interrogators could generally distinguish them from humans. This suggests the interrogators were not simply guessing randomly.
• The research indicates that interrogators often relied on social, emotional, and linguistic cues rather than traditional measures of knowledge and reasoning when trying to distinguish between humans and AI. Interestingly, providing strange prompts or using "jailbreaks" was the most effective strategy for interrogators, while asking about the weather or human experiences was least effective.
• The findings have significant social and economic implications, suggesting that contemporary LLMs could potentially substitute for humans in short conversations, raising concerns about deception, misinformation, and the potential undermining of real human interaction. The study also found that general knowledge about LLMs and frequent chatbot interaction did not consistently improve participants' ability to distinguish AI from humans.