Artificial intelligence (AI) technologies revolutionize vast fields of society. Humans using these systems are likely to expect them to work in a potentially hyperrational manner. However, in this study, we show that some AI systems, namely large language models (LLMs), exhibit behavior that strikingly resembles human-like intuition - and the many cognitive errors that come with them. We use a state-of-the-art LLM, namely the latest iteration of OpenAI's Generative Pre-trained Transformer (GPT-3.5), and probe it with the Cognitive Reflection Test (CRT) as well as semantic illusions that were originally designed to investigate intuitive decision-making in humans. Our results show that GPT-3.5 systematically exhibits "machine intuition," meaning that it produces incorrect responses that are surprisingly equal to how humans respond to the CRT as well as to semantic illusions. We investigate several approaches to test how sturdy GPT-3.5's inclination for intuitive-like decision-making is. Our study demonstrates that investigating LLMs with methods from cognitive science has the potential to reveal emergent traits and adjust expectations regarding their machine behavior.

预处理的大语言模型（LLM）广泛用于自然语言处理（NLP）的许多子场，通常被称为具有特定任务示例的优秀少数学习者。值得注意的是，思想链（COT）提示，这是一种通过分步答案示例引发复杂的多步推理的技术，在算术和符号推理中实现了最新的表演，难以置信的System-2任务不遵循LLMS的标准缩放定律。尽管这些成功通常归因于LLM的几次学习能力，但我们表明，LLM是通过在每个答案之前简单地添加“让我们逐步思考”而成为不错的零射击推理者。实验结果表明，使用相同的单个提示模板，我们的零射击功能明显优于零摄像机LLM在不同的基准推理任务上的零摄像机表现，包括算术（Multiarith，GSM8K，Aqua-Rat，SVAMP，SVAMP），符号推理（最后一个字母，字母，字母，字母，，，，，字母，字母）（最后一个字母），硬币翻转）和其他逻辑推理任务（日期理解，跟踪洗牌对象），而没有任何手工制作的几个示例，例如通过175B参数指令gpt模型将Multiarith的准确性从17.7％提高到78.7％，GSM8K从10.4％提高到40.7％，以及另一种现成的大型模型，540B参数Palm Palm的相似改进。在非常多样化的推理任务中，这个单一提示的多功能性暗示了LLM的尚未开发和研究的基本零拍功能，这表明可以通过简单提示来提取高级，多任务的广泛认知能力。我们希望我们的工作不仅可以作为具有挑战性的推理基准的最小零击基线，而且还强调了仔细探索和分析LLM中隐藏在LLM中的巨大的零拍知识的重要性，然后在制作Finetunning数据集或少数拍摄的典范之前。

推理是人类认知和智力的关键支柱。在过去的十年中，我们目睹了自然语言处理的巨大收益和大型语言模型的前所未有的缩放。最近的工作表征了很少射击技术的能力，例如思想链，可以在大语言模型中模仿人类的推理。这个标志性的功能很少，连同不断扩展的语言模型相结合，打开了解决各种任务的可能性的远景，例如数学单词问题，代码完成和常识性推理。促使思想链（COT）通过提供中间步骤并敦促模型遵循相同的过程，从而进一步推动了模型的性能。尽管具有令人信服的性能，但在这些模型中推理能力的起源却很少探索。这项工作启动了对大语言模型中推理机制的更深入了解的初步步骤。我们的工作围绕查询模型，同时在提示中控制除一个组件以外的所有组件外：符号，模式和文本。然后，我们分析查询之间的性能差异。我们的结果表明，在提示中存在事实模式对于COT的成功并不是必需的。尽管如此，我们从经验上表明，仅依靠模式也不足以获得高质量的结果。我们认为文本具有常识性知识和意义。我们详尽的经验分析提供了定性的例子，说明了文本和模式之间的共生关系。这种对COT的系统理解使我们能够设计简洁的思想链，被称为CCOT，在其中修剪文本和模式只能保留其关键角色，同时以PAR或更高的求解任务率交付。

我们探索如何产生一系列思想 - 一系列中间推理步骤 - 显着提高了大语言模型执行复杂推理的能力。特别是，我们通过一种称为“思想链”提示的简单方法在足够大的语言模型中自然出现这种推理能力，在此过程中，一些思想示范被作为提示的示例提供了。三种大语模型的实验表明，促使思想链提高了一系列算术，常识和象征性推理任务的性能。经验收益可能会引人注目。例如，仅使用八个思想范围的540B参数语言模型才能在数学单词问题的GSM8K基准上实现最新的精度，甚至超过了带有验证器的Fineted GPT-3。

Large language models (LLMs) have exploded in popularity in the past few years and have achieved undeniably impressive results on benchmarks as varied as question answering and text summarization. We provide a simple new prompting strategy that leads to yet another supposedly "super-human" result, this time outperforming humans at common sense ethical reasoning (as measured by accuracy on a subset of the ETHICS dataset). Unfortunately, we find that relying on average performance to judge capabilities can be highly misleading. LLM errors differ systematically from human errors in ways that make it easy to craft adversarial examples, or even perturb existing examples to flip the output label. We also observe signs of inverse scaling with model size on some examples, and show that prompting models to "explain their reasoning" often leads to alarming justifications of unethical actions. Our results highlight how human-like performance does not necessarily imply human-like understanding or reasoning.

大型语言模型（LLMS）具有变革性。它们是预先训练的基础模型，可以通过微调来适应许多不同的自然语言任务，以前每个任务都需要单独的网络模型。这是接近人类语言的非凡多功能性的一步。 GPT-3和最近的LAMDA可以与人类进行对话，并在最少的启动之后与许多例子进行许多主题。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。在与LLM的三次访谈中得出截然不同的结论中，这种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。实际上，LLM中似乎是智慧的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。

Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summarization. However, pre-training objectives tailored for abstractive text summarization have not been explored. Furthermore there is a lack of systematic evaluation across diverse domains. In this work, we propose pre-training large Transformer-based encoder-decoder models on massive text corpora with a new selfsupervised objective. In PEGASUS, important sentences are removed/masked from an input document and are generated together as one output sequence from the remaining sentences, similar to an extractive summary. We evaluated our best PEGASUS model on 12 downstream summarization tasks spanning news, science, stories, instructions, emails, patents, and legislative bills. Experiments demonstrate it achieves state-of-the-art performance on all 12 downstream datasets measured by ROUGE scores. Our model also shows surprising performance on low-resource summarization, surpassing previous state-of-the-art results on 6 datasets with only 1000 examples. Finally we validated our results using human evaluation and show that our model summaries achieve human performance on multiple datasets.

This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges -- summarization, and question answering -- prompt ChatGPT to produce original content (98-99%) from a single text entry and also sequential questions originally posed by Turing in 1950. The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, and overall quality. While Turing's original prose scores at least 14% below the machine-generated output, the question of whether an algorithm displays hints of Turing's truly original thoughts (the "Lovelace 2.0" test) remains unanswered and potentially unanswerable for now.

We propose the Detailed Outline Control (DOC) framework for improving long-range plot coherence when automatically generating several-thousand-word-long stories. DOC consists of two complementary components: a detailed outliner and a detailed controller. The detailed outliner creates a more detailed, hierarchically structured outline, shifting creative burden from the main drafting procedure to the planning stage. The detailed controller ensures the more detailed outline is still respected during generation by controlling story passages to align with outline details. In human evaluations of automatically generated stories, DOC substantially outperforms a strong Re3 baseline (Yang et al., 2022) on plot coherence (22.5% absolute gain), outline relevance (28.2%), and interestingness (20.7%). Humans also judged DOC to be much more controllable in an interactive generation setting.

抽象推理是智能系统的关键能力。大型语言模型在抽象推理任务上实现了高度的性能，但表现出许多缺陷。但是，人类的抽象推理也是不完美的，并且取决于我们对推理问题内容的知识和信念。例如，人类对在日常情况下基于逻辑规则的逻辑规则比关于抽象属性的任意规则更可靠地理解。语言模型的培训经验类似地赋予了他们先前的期望，这些期望反映了人类的知识和信念。因此，我们假设语言模型会显示出类似人类的内容对抽象推理问题的影响。我们在三个逻辑推理任务中探讨了这一假设：自然语言推论，判断三段论的逻辑有效性和ison选择任务（Wason，1968）。我们发现，最新的大语言模型（具有7或700亿个参数； Hoffman等，2022）反映了这些任务中人类在人类中观察到的许多相同模式 - 像人类一样，模型对可信情况的理由更有效地理由不现实或抽象的。我们的发现对理解这些认知效应以及有助于语言模型表现的因素具有影响。

尽管大型语言模型（LLMS）经常产生令人印象深刻的输出，但它们也无法推理和事实。我们着手研究这些限制如何影响LLM回答基于困难的问题的能力和理由。我们应用了与人类一致的GPT-3（指示程序）回答多项选择体检问题（USMLE和MEDMCQA）和医学研究问题（PubMedQA）。我们调查了思想链（逐步思考）提示，接地（通过搜索结果增强提示）和很少的射击（以问答的示例来准备问题）。对于USMLE问题的子集，医疗领域专家审查并注释了模型的推理。总体而言，GPT-3取得了最新的机器学习绩效的重大改进。我们观察到GPT-3通常是知识渊博的，并且可以理解医疗问题。 GPT-3当面对一个无法回答的问题时，仍将尝试回答，通常会导致偏见的预测分布。 LLM与人类绩效不相同，但我们的结果表明，与医疗问题解决的推理模式的出现。我们推测，缩放模型和数据，增强及时对齐方式以及允许更好地完成完成的上下文化将足以使LLMS在此类任务上达到人级的性能。

语言模型在需要自然语言理解的各种任务上取得了非凡的表现。然而，最先进的模型通常在需要定量推理的任务上挣扎，例如在大学一级解决数学，科学和工程问题。为了帮助缩小这一差距，我们介绍了Minerva，Minerva是一种在一般自然语言数据上鉴定的大型语言模型，并进一步培训了技术内容。该模型在不使用外部工具的情况下实现了技术基准测试的最新性能。我们还评估了我们在需要定量推理的物理学，生物学，化学，经济学和其他科学方面的200多个本科生问题上评估我们的模型，并发现该模型可以正确回答其中几乎三分之一。

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications.

当前的语言模型可以产生高质量的文本。他们只是复制他们之前看到的文本，或者他们学习了普遍的语言抽象吗？要取笑这些可能性，我们介绍了乌鸦，这是一套评估生成文本的新颖性，专注于顺序结构（n-gram）和句法结构。我们将这些分析应用于四种神经语言模型（LSTM，变压器，变换器-XL和GPT-2）。对于本地结构 - 例如，单个依赖性 - 模型生成的文本比来自每个模型的测试集的人类生成文本的基线显着不那么新颖。对于大规模结构 - 例如，总句结构 - 模型生成的文本与人生成的基线一样新颖甚至更新颖，但模型仍然有时复制，在某些情况下，在训练集中重复超过1000字超过1,000字的通道。我们还表现了广泛的手动分析，表明GPT-2的新文本通常在形态学和语法中形成良好，但具有合理的语义问题（例如，是自相矛盾）。

我们介绍了Sparrow，这是一个寻求信息的对话代理，与提示的语言模型基线相比，训练有素，更有帮助，正确和无害。我们使用从人类反馈中的强化学习来培训我们的模型，以帮助人类评估者判断代理人的行为。首先，为了使我们的代理人更有帮助和无害，我们将良好对话的要求分解为代理人应遵循的自然语言规则，并分别向评估者询问每个规则。我们证明，这种崩溃使我们能够收集对代理行为的更多针对性的人类判断，并允许更有效的规则条件奖励模型。其次，我们的代理商在收集对模型声明的偏好判决时提供了支持事实主张的来源的证据。对于事实问题，麻雀提供的证据支持了78％的时间。比基线比基线更享受麻雀，同时对人类的对抗性探测更具弹性，在探测时只有8％的时间违反了我们的规则。最后，我们进行了广泛的分析，表明尽管我们的模型学会遵守我们的规则，但它可以表现出分布偏见。

There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a 'good' explanation. There exists vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations towards the explanation process. This paper argues that the field of explainable artificial intelligence should build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.

最近已被证明大型语言模型在各种任务集中获得合理的零射普通化（Brown等，2020）。它已经假设这是语言模型的隐式多任务学习的结果，在语言模型中的预押（Radford等，2019）。可以通过明确的多任务学习直接引起零拍常规化？为了以缩放测试这个问题，我们开发一个系统，以便轻松地将任何自然语言任务映射到人类可读的提示表单中。我们转换一组大量的监督数据集，每个数据集都有多个提示，具有不同的措辞。这些提示的数据集允许基准测试模型执行完全看不见的任务的能力。我们介绍了一个普拉克尔编码器 - 解码器模型（Raffel等，2020; Lester等，2021），覆盖各种任务。该模型在多个标准数据集中达到强大的零点性能，通常优于其尺寸的型号超过16倍。此外，我们的方法对来自Big-替补基准测试的任务子集具有强烈性能，优于其尺寸的6倍。所有提示和培训的型号都可以在https://github.com/ bigscience-workshop / protectsource / httpsource / https：//huggingface.co/bigscience/t0pp。

神经网络语言模型的最新进展表明，通过利用大规模自然语言数据中的语言关联来得出表达意义表示。这些潜在的格式塔表示已实现许多实际应用的最新性能。看来我们正处于经验得出强大而表达的可计算语义的途径。出现的一个关键问题是，仅语言数据才能使计算机能够理解有关物理世界的必要真相？必须关注这个问题，因为我们与智能机器的未来相互作用取决于我们的技术正确地表示和处理人类通常观察到的概念（对象，属性和过程）。在审查了现有协议之后，这项工作的目的是使用新颖且严格控制的推理测试探索这个问题，并突出显示哪些模型可能直接从纯语言数据中学习。

When a large language model (LLM) performs complex reasoning by chain of thought (CoT), it can be highly sensitive to individual mistakes. We have had to train verifiers to address this issue. As we all know, after human inferring a conclusion, they often check it by re-verifying it, which can avoid some mistakes. We propose a new method called self-verification that uses the conclusion of the CoT as a condition to build a new sample and asks the LLM to re-predict the original conditions which be masked. We calculate an explainable verification score based on the accuracy. This method can improve the accuracy of multiple arithmetics and logical reasoning datasets when using few-shot learning. we have demonstrated that LLMs can conduct explainable self-verification of their own conclusions and achieve competitive reasoning performance. Extensive experimentals have demonstrated that our method can help multiple large language models with self-verification can avoid interference from incorrect CoT. Code is available at \url{https://github.com/WENGSYX/Self-Verification}

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.