The recent advent of large language models - large neural networks trained on a simple predictive objective over a massive corpus of natural language - has reinvigorated debate over whether human cognitive capacities might emerge in such generic models given sufficient training data. Of particular interest is the ability of these models to reason about novel problems zero-shot, without any direct training on those problems. In human cognition, this capacity is closely tied to an ability to reason by analogy. Here, we performed a direct comparison between human reasoners and a large language model (GPT-3) on a range of analogical tasks, including a novel text-based matrix reasoning task closely modeled on Raven's Progressive Matrices. We found that GPT-3 displayed a surprisingly strong capacity for abstract pattern induction, matching or even surpassing human capabilities in most settings. Our results indicate that large language models such as GPT-3 have acquired an emergent ability to find zero-shot solutions to a broad range of analogy problems.

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。

本文演示了通过对自动调整自选语言模型（GPT-NEO）进行适当的逐步演示，可以将其执行以前证明变换器的数学任务 - 龙手模数操作 - 具有相对较少的例子。具体而言，我们微调GPT-Neo从DeepMind数学数据集解决数字_Div_remainder任务;萨克斯顿等人。 （ARXIV：1904.01557）报告，这项任务的准确性低于40％，培训例子有200万。我们表明，在200次适当地结构化的练习型展示远期问题并报告剩余时间后，最小可用的GPT-Neo模型可实现80％以上。这是通过构建用于微调的适当数据集来实现，没有更改学习算法。这些结果表明，小型精心设计演示的微调自回归语言模型可能是一种有用的范例，可以在没有机器学习中培训的情况下使个人能够培训，以便在这些模型中执行某种复杂的多步任务。

人们容易概括到新型域和刺激的知识。我们提出了一种在计算模型中实例化的理论，基于跨域人类中的跨域泛化是对结构化（即，象征性）关系表示的模拟推断的情况。该模型是LISA和关系推论和学习的DORA模型的延伸。生成的模型在没有监控的情况下，从非关系输入中的关系和格式（即结构）（即，结构）既与强化学习的容量增强，利用这些表示来学习单个域，然后向新域推广首先通过模拟推理（即零拍摄学习）。我们展示了模型从各种简单的视觉刺激学习结构化关系表示的能力，并在视频游戏（突破和乒乓球）和几个心理任务之间进行跨域泛化。我们展示了模型的轨迹在学到关系时，旨在让孩子的轨迹镜头紧密地镜子，从文学中占据了儿童推理和类比制作的文献中的现象。该模型在域之间的概括能力展示了在其基础关系结构方面代表域的灵活性，而不是简单地就其投入和产出之间的统计关系而言。

Out-of-distribution generalization (OODG) is a longstanding challenge for neural networks. This challenge is quite apparent in tasks with well-defined variables and rules, where explicit use of the rules could solve problems independently of the particular values of the variables, but networks tend to be tied to the range of values sampled in their training data. Large transformer-based language models have pushed the boundaries on how well neural networks can solve previously unseen problems, but their complexity and lack of clarity about the relevant content in their training data obfuscates how they achieve such robustness. As a step toward understanding how transformer-based systems generalize, we explore the question of OODG in small scale transformers trained with examples from a known distribution. Using a reasoning task based on the puzzle Sudoku, we show that OODG can occur on a complex problem if the training set includes examples sampled from the whole distribution of simpler component tasks. Successful generalization depends on carefully managing positional alignment when absolute position encoding is used, but we find that suppressing sensitivity to absolute positions overcomes this limitation. Taken together our results represent a small step toward understanding and promoting systematic generalization in transformers.

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）。

Recent times have witnessed an increasing number of applications of deep neural networks towards solving tasks that require superior cognitive abilities, e.g., playing Go, generating art, question answering (such as ChatGPT), etc. Such a dramatic progress raises the question: how generalizable are neural networks in solving problems that demand broad skills? To answer this question, we propose SMART: a Simple Multimodal Algorithmic Reasoning Task and the associated SMART-101 dataset, for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed specifically for children in the 6-8 age group. Our dataset consists of 101 unique puzzles; each puzzle comprises a picture and a question, and their solution needs a mix of several elementary skills, including arithmetic, algebra, and spatial reasoning, among others. To scale our dataset towards training deep neural networks, we programmatically generate entirely new instances for each puzzle while retaining their solution algorithm. To benchmark the performance on the SMART-101 dataset, we propose a vision and language meta-learning model using varied state-of-the-art backbone neural networks. Our experiments reveal that while powerful deep models offer reasonable performances on puzzles that they are trained on, they are not better than random accuracy when analyzed for generalization. We also evaluate the recent ChatGPT large language model on a subset of our dataset and find that while ChatGPT produces convincing reasoning abilities, the answers are often incorrect.

Large language models have recently shown promising progress in mathematical reasoning when fine-tuned with human-generated sequences walking through a sequence of solution steps. However, the solution sequences are not formally structured and the resulting model-generated sequences may not reflect the kind of systematic reasoning we might expect an expert human to produce. In this paper, we study how to build stronger reasoning capability in language models using the idea of relational abstractions. We introduce new types of sequences that more explicitly provide an abstract characterization of the transitions through intermediate solution steps to the goal state. We find that models that are supplied with such sequences as prompts can solve tasks with a significantly higher accuracy, and models that are trained to produce such sequences solve problems better than those that are trained with previously used human-generated sequences and other baselines. Our work thus takes several steps toward elucidating and improving how language models perform on tasks requiring multi-step mathematical reasoning.

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.

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。

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。

We demonstrate that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even becoming competitive with prior state-ofthe-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous nonsparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks. We also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora.

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.

我们建议并探讨可以将语言模型作为社会科学研究中特定人类亚人群的有效代理进行研究的可能性。人工智能工具的实践和研究应用有时受到有问题的偏见（例如种族主义或性别歧视）的限制，这些偏见通常被视为模型的统一特性。我们表明，一个这样的工具中的“算法偏见”（GPT-3语言模型）既是细粒度又是人口统计相关的，这意味着适当的条件会导致其准确地仿真来自各种人类的响应分布亚组。我们将此属性称为“算法忠诚度”，并在GPT-3中探索其范围。我们通过将模型调节在美国进行的多项大型调查中的数千个社会人口统计背景故事中调节，从而创建“硅样本”。然后，我们比较硅和人类样品，以证明GPT-3中包含的信息远远超出了表面相似性。它是细微的，多方面的，并反映了特征人类态度的思想，态度和社会文化背景之间的复杂相互作用。我们建议，具有足够算法的忠诚度的语言模型构成了一种新颖而有力的工具，可以促进各种学科的人类和社会的理解。

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。

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。

Developing safe and useful general-purpose AI systems will require us to make progress on scalable oversight: the problem of supervising systems that potentially outperform us on most skills relevant to the task at hand. Empirical work on this problem is not straightforward, since we do not yet have systems that broadly exceed our abilities. This paper discusses one of the major ways we think about this problem, with a focus on how to turn it into one that can be productively studied empirically. We first present an experimental design centered on choosing tasks for which human specialists succeed but unaided humans and current general AI systems fail. We then present a proof-of-concept experiment following meant to demonstrate a key feature of this experimental design and show its viability with two question-answering tasks: MMLU and time-limited QuALITY. On these tasks, we find that human participants who interact with an unreliable large-language-model dialog assistant through chat -- a trivial baseline strategy for scalable oversight -- substantially outperform both the model alone and their own unaided performance. These results are an encouraging sign that scalable oversight will be tractable to study with present models and bolster recent findings that large language models can productively assist humans with difficult tasks.

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。

鉴于大型语言模型的广泛能力，应该有可能朝着一般的文本的助手工作，这些助手与人类价值一致，这意味着它是有帮助，诚实的和无害的。在此方向上的初始遗传，我们研究简单的基线技术和评估，例如提示。我们发现，从模型规模增加适度的干预措施的好处，概括为各种对准评估，并不会损害大型模型的性能。接下来，我们调查与对齐，比较仿制，二进制歧视和排名偏好建模相关的几个培训目标的缩放趋势。我们发现排名优先级模型比模仿学习更好地表现得多，并且通常以模型大小更有利地缩放。相比之下，二进制歧视通常与模仿学习非常类似地执行和缩放。最后，我们研究了一种“偏好模型预训练阶段的培训阶段，其目的是在对人偏好的芬明时提高样本效率。

随着人工智能系统变得越来越强大和普遍，人们对机器的道德或缺乏道德的关注变得越来越关注。然而，向机器讲授道德是一项艰巨的任务，因为道德仍然是人类中最激烈的争论问题之一，更不用说AI了。但是，部署到数百万用户的现有AI系统已经在做出充满道德影响的决策，这构成了一个看似不可能的挑战：教学机器的道德意义，而人类继续努力努力。为了探索这一挑战，我们介绍了Delphi，这是一个基于深层神经网络的实验框架，直接训练了描述性道德判断，例如，“帮助朋友”通常是不错的，而“帮助朋友传播假新闻”不是。经验结果提供了对机器伦理的承诺和局限性的新见解。面对新的道德情况，德尔菲（Delphi）表现出强大的概括能力，而现成的神经网络模型表现出明显差的判断，包括不公正的偏见，证实了对明确教学机器的道德意义的必要性。然而，德尔菲并不完美，表现出对普遍性偏见和不一致的敏感性。尽管如此，我们还是展示了不完美的Delphi的积极用例，包括在其他不完美的AI系统中将其用作组件模型。重要的是，我们根据著名的道德理论来解释Delphi的运营化，这使我们提出了重要的未来研究问题。