Connectionists: Large Language Models and the Reverse Turing Test

[Terry Sejnowski]

*Large Language Models and the Reverse Turing Test
**
https://arxiv.org/abs/2207.14382**

*
Terrence Sejnowski 
<https://arxiv.org/search/cs?searchtype=author&query=Sejnowski%2C+T>

    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 ranger 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 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.

    LLMs can talk the talk, but can they walk the walk?

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