ArticleNeural computation2023
Large Language Models and the Reverse Turing Test.
Article in Neural computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed.
- The cognitive biases that may exacerbate inflationary and deflationary positions about large language models.Current opinion in behavioral sciences · 2026Review
- Beyond computational equivalence: the behavioral inference principle for machine consciousness.Neuroscience of consciousness · 2026Review
- Navigating the new frontier: growth, integrity, and our vision for 2026.Abdominal radiology (New York) · 2026Article
- Metacognition of ChatGPT in confidence judgements.Frontiers in artificial intelligence · 2026Article
- Article
- Augmenting intensive care unit nursing practice with generative AI: A formative study of diagnostic synergies using simulation-based clinical cases.Journal of clinical nursing · 2025Article
- The role of trustworthy and reliable AI for multiple sclerosis.Frontiers in digital health · 2025Article
- Moving in an Uncertain World: Robust and Adaptive Control of Locomotion from Organisms to Machine Intelligence.Integrative and comparative biology · 2024Review
- Deep learning application in prediction of cancer molecular alterations based on pathological images: a bibliographic analysis via CiteSpace.Journal of cancer research and clinical oncology · 2024Article
- Active inference goes to school: the importance of active learning in the age of large language models.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Transformers and cortical waves: encoders for pulling in context across time.Trends in neurosciences · 2024Review
- The new frontier: utilizing ChatGPT to expand craniofacial research.Archives of craniofacial surgery · 2024Article
- Attributions toward artificial agents in a modified Moral Turing Test.Scientific reports · 2024Article
- GPT-4 passes the bar exam.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2024Article
- The application and challenges of ChatGPT in educational transformation: New demands for teachers' roles.Heliyon · 2024Review
- Eight challenges in developing theory of intelligence.Frontiers in computational neuroscience · 2024Article
- Embodiment and agency in a digital world.Frontiers in psychology · 2024Article
- Assessing the performance of large language models in literature screening for pharmacovigilance: a comparative study.Frontiers in drug safety and regulation · 2024Article
- The emergence of economic rationality of GPT.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Hallucination or Confabulation? Neuroanatomy as metaphor in Large Language Models.PLOS digital health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
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Abstract
Large language models (LLMs) have been transformative. They are pretrained foundational models that are self-supervised and can be adapted with fine-tuning to a wide range of natural language tasks, each of which previously would have required a separate network model. This is one step closer to the extraordinary versatility of human language. GPT-3 and, more recently, LaMDA, both of them LLMs, can carry on dialogs with humans on many topics after minimal priming with a few examples. However, there has been a wide range of reactions and debate on whether these LLMs understand what they are saying or exhibit signs of intelligence. This high variance is exhibited in three interviews with LLMs reaching wildly different conclusions. A new possibility was uncovered that could explain this divergence. What appears to be intelligence in LLMs may in fact be a mirror that reflects the intelligence of the interviewer, a remarkable twist that could be considered a reverse Turing test. If so, then by studying interviews, we may be learning more about the intelligence and beliefs of the interviewer than the intelligence of the LLMs. As LLMs become more capable, they may transform the way we interact with machines and how they interact with each other. Increasingly, LLMs are being coupled with sensorimotor devices. LLMs can talk the talk, but can they walk the walk? A road map for achieving artificial general autonomy is outlined with seven major improvements inspired by brain systems and how LLMs could in turn be used to uncover new insights into brain function.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.