论文标题
大型语言模型和反向图灵测试
Large Language Models and the Reverse Turing Test
论文作者
论文摘要
大型语言模型（LLMS）具有变革性。它们是预先训练的基础模型，可以自我监督，可以通过微调来适应各种自然语言任务，以前每个任务都需要单独的网络模型。这比人类语言的多功能性更近一步。 GPT-3和最近的LAMDA可以在最少的启动之后与人类进行许多主题进行对话，并进行一些例子。但是，关于这些LLM是否了解他们在说什么或表现出智力迹象的反应和辩论。在与LLM的三次访谈中得出了截然不同的结论，这一较高的差异显示出来。发现了一种新的可能性，可以解释这种分歧。在LLM中似乎是智力的实际上可能是反映面试官智力的镜子，这是一个显着的转折，可以被视为反向图灵测试。如果是这样，那么通过研究访谈，我们可能会更多地了解面试官的智力和信念，而不是LLM的智能。随着LLM的功能越来越能力，它们可能会改变我们与机器互动以及它们如何相互作用的方式。 LLM越来越多地与感觉运动设备耦合。 LLM可以讲话，但是他们可以走路吗？概述了实现人为的一般自治的路线图，其七项重大改进灵感来自大脑系统。 LLM可以通过在自然行为期间下载大脑数据来发现对大脑功能的新见解。
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.
