Large Language Models and the Reverse Turing Test

Abstract

Large Language Models (LLMs) have been transformative. They are pre-trained 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 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. LLMs could be used to uncover new insights into brain function by downloading brain data during natural behaviors.

Are LLMs stochastic parrots?

References in corpus (6)

• PaLM: Scaling Language Modeling with Pathways
• LaMDA: Language Models for Dialog Applications
• Training Compute-Optimal Large Language Models
• The Unreasonable Effectiveness of Deep Learning in Artificial Intelligence
• Compute Trends Across Three Eras of Machine Learning
• Inner Monologue: Embodied Reasoning through Planning with Language Models

Cited by in corpus (9)

• The Debate Over Understanding in AI's Large Language Models
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• Observations on LLMs for Telecom Domain: Capabilities and Limitations
• From Bytes to Biases: Investigating the Cultural Self-Perception of Large Language Models
• Eight challenges in developing theory of intelligence
• Practically implementing an LLM-supported collaborative vulnerability remediation process: a team-based approach
• A challenge in A(G)I, cybernetics revived in the Ouroboros Model as one algorithm for all thinking
• Meta predictive learning model of languages in neural circuits
• Imitation Game for Adversarial Disillusion with Chain-of-Thought Reasoning in Generative AI