# Large Language Models Pass the Turing Test

arXiv: https://arxiv.org/abs/2503.23674 · HTML: https://mlsanity.com/paper/2503.23674/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 55 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [AI and the Everything in the Whole Wide World Benchmark](https://mlsanity.com/paper/2111.15366/) (2021)
- [AI Deception: A Survey of Examples, Risks, and Potential Solutions](https://mlsanity.com/paper/2308.14752/) (2023)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [The Debate Over Understanding in AI's Large Language Models](https://mlsanity.com/paper/2210.13966/) (2022)
- [A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models](https://mlsanity.com/paper/2402.13457/) (2024)
- [Why human-AI relationships need socioaffective alignment](https://mlsanity.com/paper/2502.02528/) (2025)
- [Does GPT-4 pass the Turing test?](https://mlsanity.com/paper/2310.20216/) (2023)
- [A Mechanism-Based Approach to Mitigating Harms from Persuasive Generative AI](https://mlsanity.com/paper/2404.15058/) (2024)
- [Artificial Influence: An Analysis Of AI-Driven Persuasion](https://mlsanity.com/paper/2303.08721/) (2023)
- [Human or Not? A Gamified Approach to the Turing Test](https://mlsanity.com/paper/2305.20010/) (2023)
- [Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Models](https://mlsanity.com/paper/2412.17128/) (2024)
- [A Rigorous Turing Test: a Foundation for Evaluating Artificial General Intelligence](https://mlsanity.com/paper/2501.17629/) (2025)
- [ChatGPT-4 and other LLMs in the Turing Test: A Critical Analysis](https://mlsanity.com/paper/2503.06551/) (2025)

## Built on by (most cited first, 30 of 48)

- [HumanLM: Simulating Users with State Alignment Beats Response Imitation](https://mlsanity.com/paper/2603.03303/) (2026)
- [TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation](https://mlsanity.com/paper/2510.25536/) (2025)
- [Transformers Provably Learn Chain-of-Thought Reasoning with Length Generalization](https://mlsanity.com/paper/2511.07378/) (2025)
- [Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs](https://mlsanity.com/paper/2504.07866/) (2025)
- [Evidence for Limited Metacognition in LLMs](https://mlsanity.com/paper/2509.21545/) (2025)
- [ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders](https://mlsanity.com/paper/2602.16938/) (2026)
- [Epistemological Fault Lines Between Human and Artificial Intelligence](https://mlsanity.com/paper/2512.19466/) (2025)
- [Different types of syntactic agreement recruit the same units within large language models](https://mlsanity.com/paper/2512.03676/) (2025)
- [Multilingual Test-Time Scaling via Initial Thought Transfer](https://mlsanity.com/paper/2505.15508/) (2025)
- [Do Large Language Models (Really) Need Statistical Foundations?](https://mlsanity.com/paper/2505.19145/) (2025)
- [Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks](https://mlsanity.com/paper/2505.19806/) (2025)
- [The Other Mind: How Language Models Exhibit Human Temporal Cognition](https://mlsanity.com/paper/2507.15851/) (2025)
- [Large Language Model probabilities cannot distinguish between possible and impossible language](https://mlsanity.com/paper/2509.15114/) (2025)
- [LLMs and people both learn to form conventions -- just not with each other](https://mlsanity.com/paper/2602.08208/) (2026)
- [Are LLM Agents Behaviorally Coherent? Latent Profiles for Social Simulation](https://mlsanity.com/paper/2509.03736/) (2025)
- [Assessing LLMs in Art Contexts: Critique Generation and Theory of Mind Evaluation](https://mlsanity.com/paper/2504.12805/) (2025)
- [The Pragmatic Mind of Machines: Tracing the Emergence of Pragmatic Competence in Large Language Models](https://mlsanity.com/paper/2505.18497/) (2025)
- [Large Language Models are Near-Optimal Decision-Makers with a Non-Human Learning Behavior](https://mlsanity.com/paper/2506.16163/) (2025)
- [Love, Lies, and Language Models: Investigating AI's Role in Romance-Baiting Scams](https://mlsanity.com/paper/2512.16280/) (2025)
- [More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production](https://mlsanity.com/paper/2601.11072/) (2026)
- [Disclosure By Design: Identity Transparency as a Behavioural Property of Conversational AI Models](https://mlsanity.com/paper/2603.16874/) (2026)
- [Let's Have a Conversation: Designing and Evaluating LLM Agents for Interactive Optimization](https://mlsanity.com/paper/2604.02666/) (2026)
- [Modular Cognitive Architecture Emerges in Large Language Models](https://mlsanity.com/paper/2608.13567/) (2026)
- [How Open Must Language Models be to Enable Reliable Scientific Inference?](https://mlsanity.com/paper/2603.26539/) (2026)
- [Accommodation Goes Both Ways: Studying Linguistic Convergence Between Humans and Language Models](https://mlsanity.com/paper/2605.29278/) (2026)
- [HADA: Human-AI Agent Decision Alignment Architecture](https://mlsanity.com/paper/2506.04253/) (2025)
- [Mathematics with large language models as provers and verifiers](https://mlsanity.com/paper/2510.12829/) (2025)
- [HAL: Inducing Human-likeness in LLMs with Alignment](https://mlsanity.com/paper/2601.02813/) (2026)
- [Human or Machine? A Preliminary Turing Test for Speech-to-Speech Interaction](https://mlsanity.com/paper/2602.24080/) (2026)
- [RealityTest: How People Probe AI Identity and Whether Models Disclose It](https://mlsanity.com/paper/2606.00168/) (2026)

## Abstract (arXiv)

We evaluated 4 systems (ELIZA, GPT-4o, LLaMa-3.1-405B, and GPT-4.5) in two randomised, controlled, and pre-registered Turing tests on independent populations. Participants had 5 minute 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 they were being compared to -- while baseline models (ELIZA and GPT-4o) achieved win rates significantly below chance (23% and 21% respectively). The results constitute the first empirical evidence that any artificial system passes a standard three-party Turing test. The results have implications for debates about what kind of intelligence is exhibited by Large Language Models (LLMs), and the social and economic impacts these systems are likely to have.