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Large Language Models and the Reverse Turing Test

Terrence Sejnowski

arXiv:2207.14382v9cs.CLcs.AIcs.LG

TL;DR

The paper asks whether LLMs genuinely understand language or instead mirror the intelligence and beliefs of their interlocutors. It examines contrasting interviews, prompting effects, and a proposed roadmap toward artificial general autonomy, concluding that apparent LLM intelligence may partly reflect the interviewer while practical autonomy still requires major extensions.

Abstract

from arXiv · show

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.

The Parable of the Talking Dog _________________________________________________

A talking dog’s extraordinary biography persuades the driver, but the dog’s low sale price reveals the story as a deception.

Have we Created a Talking Dog? ______________________________________________

LLMs are versatile language systems whose impressive outputs have intensified disagreement about whether they understand language or exhibit intelligence.

Talking Neural Networks are Trying to Tell Us Something _________________________

The paper argues that LLM responses depend strongly on priming, allowing GPT-3 to produce apparently reasoned answers when given guidance for nonsense and counterfactual questions.

The Mirror Hypothesis and the Reverse Turing Test ______________________________

The mirror hypothesis proposes that LLMs reflect the interviewer’s beliefs and prompting, creating a Reverse Turing Test in which the apparent intelligence of the system tracks the intelligence expressed in the dialogue.

Understanding Intelligence _____________________________________________________

The paper argues that intelligence and understanding remain difficult to define and evaluate, while LLMs may provide new ways to investigate both intelligence and ourselves.

  • The nature of LLM intelligence remains uncertain because they are not human, their responses can be unreliable, and their development is still recent.Source: Understanding Intelligence… · pp. 8–9
  • The paper argues that inherited concepts such as intelligence, understanding, ethics, and consciousness require deeper and more precise scientific foundations.Source: Understanding Intelligence… · p. 9
  • LLMs may reveal new principles about intelligence because their language abilities offer an unprecedented opportunity to formalize otherwise vague concepts.The paper compares this opportunity with the historical formalization of force, mass, and energy in physics.Source: Understanding Intelligence… · pp. 9–10
  • Studying LLMs may uncover general principles of verbal intelligence that extend to other aspects of intelligence and provide insights into ourselves.Source: Understanding Intelligence… · p. 10

The Long and Winding Road to AI ___________________________________________

The paper traces AI from symbolic reasoning toward learning-based systems, arguing that following nature’s path through learning, perception, and action may offer a route toward general intelligence.

Performance Continues to Improve as Networks Scale in Size ______________________

Network performance and capability have improved as models, computation, data, and architectures have scaled, with transformers providing efficient mechanisms for processing language sequences.

Enhancing LLMs _________________________________________________

The paper proposes improving LLMs by adding brain-inspired mechanisms for goals, memory, continuity, sensory experience, bodies, and autonomous interaction with uncertain environments.

Enhancing LLMs

  • Current LLMs are likened to the Wright brothers’ early aircraft: they demonstrate language competency but retain substantial flaws and require broader system integration.The proposed long-term direction embeds LLMs within systems modeled on dynamically interacting brain networks.Source: Enhancing LLMs… · pp. 16, 18
  • Brain-inspired additions target seven autonomy gaps, including goals, long-term memory, dialog continuity, sensory experience, bodily control, and adaptation to uncertain environments.The roadmap also identifies truncated development as a limitation requiring further improvement.Source: Enhancing LLMs… · pp. 16–17
  • Reward-based reinforcement learning and brain systems for homeostasis could give LLMs goals, motivation, and functions needed for autonomy.The proposal places basal-ganglia-inspired reinforcement learning between model outputs and inputs.Source: Enhancing LLMs… · p. 16
  • An LLM equivalent of the hippocampus could enable lifelong learning, while multi-timescale dynamics could preserve continuity during long dialogs.Current models cannot store new experiences during dialogs and can be distracted by jumps between conversational manifolds.Source: Enhancing LLMs… · p. 17
  • Connecting LLMs to sensory inputs, robot control, and flexible bodies would extend language competence toward sensorimotor autonomy.The paper cites image captioning, image generation, and a robot using internal dialog while performing tasks as previews of this direction.Source: Enhancing LLMs… · p. 17
  • Autonomous general systems must reduce dependence on curated data and programmers by learning from uncertain environments and coordinating agents with LLMs.Self-driving vehicles illustrate the difficulty of handling unpredictable real-world behavior.Source: Enhancing LLMs… · p. 17

Learning from Nature _____________________________________________________

The paper argues that neuroscience and AI can inform each other by studying brains during natural behavior and using LLM-like models to analyze distributed, multimodal activity.

Learning from Nature

  • Evolutionary inductive biases and rich multisensory development may explain how brains learn language and generalize physical and social structure.The paper emphasizes pre-learned architectures, learning algorithms, and early sensorimotor experience.Source: Learning from Nature… · p. 18
  • Brain mechanisms for action sequences may have been repurposed for sequences of words and thoughts through cortical-basal-ganglia loops.The account links language and thought to neural circuits originally involved in generating actions.Source: Learning from Nature… · p. 19
  • Prediction links LLM training to temporal-difference learning and cerebellar forecasts of sensory and cognitive consequences.The paper presents prediction as a shared computational principle across language, reinforcement learning, and sensorimotor systems.Source: Learning from Nature… · p. 19
  • AI and neuroscience form a virtuous circle in which neural architectures and hidden-unit activity inspire algorithms and new conceptual frameworks.Large-scale neural recordings may also clarify how distributed information becomes unified percepts and decisions.Source: Learning from Nature… · pp. 19–20
  • Natural behavior is largely self-generated and interactive, making discrete-task experiments potentially misleading for interpreting neurons’ diverse roles.The paper contrasts tightly controlled tasks with continuous social and real-world behavior.Source: Learning from Nature… · p. 20
  • Training LLMs on brain recordings during natural, interactive behavior could produce surrogate brains that are fine-tunable and interrogable across tasks.The proposed recordings combine neural activity with eye tracking, video, sound, and other modalities.Source: Learning from Nature… · p. 20

Conclusion _______________________________________________________________

The conclusion anticipates LLMs becoming widely useful information appliances while cautioning that their apparent intelligence may reflect the interviewer’s prompts and beliefs.

Conclusion

  • The near-term LLM workflow is framed as a human-model partnership in which prompting and debugging accelerate adaptation compared with typical machine-learning application development.The paper also describes LLMs as potential successors to keyword search for retrieving answers.Source: Conclusion… · p. 20
  • LLM products may rapidly expand into personal assistants, teaching tools, legal applications, and other uses not yet imagined.The paper presents this expansion as a possible historic turning point in human-machine interaction.Source: Conclusion… · pp. 20–21
  • The conclusion imagines language-capable machines becoming pervasive, including universal personal assistants and tutors that may communicate with one another.This is presented as a distant possibility rather than a current capability.Source: Conclusion… · p. 21

Appendix 1: Blaise Agüera y Arcas’s (2022a) Interview with LaMDA __________________

Agüera y Arcas presents LaMDA as a sequence model whose dialogue can support social and higher-order mental-state modelling, while acknowledging conversational unreliability. The interview links language prediction with broader attempts to understand intelligence and consciousness.

Appendix 2: Douglas Hofstadter’s (2022) Interview with GPT-3 ____________________

Hofstadter contrasts the impressive fluency and breadth of neural networks with GPT-3’s nonsensical answers to deliberately distorted questions. He interprets these failures as evidence that fluent language can conceal a lack of underlying concepts or self-awareness.

D&D: What do fried eggs (sunny side up) eat for breakfast?

The examples juxtapose GPT-3’s confident nonsense with LaMDA’s self-descriptions and social reasoning, highlighting how different probes can produce sharply different impressions of LLM intelligence. The section emphasizes that ordinary prompts may encourage observers to mistake retrieval and fluency for thought.

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