我们建议一种使用大型语言模型(例如GPT-3)在给定情况下模拟不同人类的响应的方法。我们通过尝试重现公认的经济,心理语言和社会实验来测试我们的方法。该方法需要每个实验的及时模板。模拟是通过改变(假设的)主题细节(例如名称)和分析语言模型产生的文本来运行的。我们通过使用GPT-3来验证我们的方法,以表明可以模拟不同人的反应,并且他们的反应与文献中先前的人类研究一致。我们发现,较大的语言模型生成的分布与先前的实验结果更好地保持一致,这表明一种趋势,即未来的语言模型可以用于对人类反应的更忠实的模拟。我们将语言模型用于模拟的使用与对语言模型的拟人化观点形成对比。
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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。
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As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90-100% of labels, sometimes more so than corresponding human-written datasets. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user's preferred answer ("sycophancy") and express greater desire to pursue concerning goals like resource acquisition and goal preservation. We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down. Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors.
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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。
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Many real-world applications of language models (LMs), such as code autocomplete and writing assistance, involve human-LM interaction, but the main LM benchmarks are non-interactive, where a system produces output without human intervention. To evaluate human-LM interaction, we develop a framework, Human-AI Language-based Interaction Evaluation (H-LINE), that expands non-interactive evaluation along three dimensions, capturing (i) the interactive process, not only the final output; (ii) the first-person subjective experience, not just a third-party assessment; and (iii) notions of preference beyond quality. We then design five tasks ranging from goal-oriented to open-ended to capture different forms of interaction. On four state-of-the-art LMs (three variants of OpenAI's GPT-3 and AI21's J1-Jumbo), we find that non-interactive performance does not always result in better human-LM interaction and that first-person and third-party metrics can diverge, suggesting the importance of examining the nuances of human-LM interaction.
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大型语言模型会产生类似人类的文本,这些文本推动了越来越多的应用。但是,最近的文献以及越来越多的现实世界观察表明,这些模型可以产生有毒,有偏见,不真实或其他有害的语言。尽管正在进行评估语言模型危害的工作,但要远见卓识转换出可能出现的危害可能会引起严格的基准。为了促进这种翻译,我们概述了六种表征有害文本的方式,这些方法在设计新基准时值得明确考虑。然后,我们将这些特征用作镜头来识别现有基准中的趋势和差距。最后,我们将它们应用于视角API的案例研究,这是一种毒性分类器,被广泛用于HARS基准。我们的特征提供了一块桥梁,可以在远见和有效评估之间转化。
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As text generated by large language models proliferates, it becomes vital to understand how humans engage with such text, and whether or not they are able to detect when the text they are reading did not originate with a human writer. Prior work on human detection of generated text focuses on the case where an entire passage is either human-written or machine-generated. In this paper, we study a more realistic setting where text begins as human-written and transitions to being generated by state-of-the-art neural language models. We show that, while annotators often struggle at this task, there is substantial variance in annotator skill and that given proper incentives, annotators can improve at this task over time. Furthermore, we conduct a detailed comparison study and analyze how a variety of variables (model size, decoding strategy, fine-tuning, prompt genre, etc.) affect human detection performance. Finally, we collect error annotations from our participants and use them to show that certain textual genres influence models to make different types of errors and that certain sentence-level features correlate highly with annotator selection. We release the RoFT dataset: a collection of over 21,000 human annotations paired with error classifications to encourage future work in human detection and evaluation of generated text.
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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)。
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语言模型可以根据给定的文化背景产生有害和偏置的输出并表现出不良行为。我们提出了一种将语言模型适应社会(PALM)与值目标数据集的过程,以通过在反映预定的一组目标值集合的数据集上进行制备和微调来显着地改变模型行为的迭代过程。我们使用三个指标评估我们的进程:具有人类评估的定量指标,将输出遵守目标值,毒性评分对产出;和定性度量分析与给定社会类别相关的最常见的单词。通过每次迭代,我们根据来自评估的观察到的缺点添加其他培训数据集示例。与基线和控制模型相比,PALMS在所有指标上显着更好地为广泛的GPT-3语言模型尺寸进行了基线和控制模型,而不会影响能力完整性。我们发现PALMS的有效性随模型规模而增加。我们表明,显着调整语言模型行为与小型手腕策划数据集是可行的。
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情绪分析中最突出的任务是为文本分配情绪,并了解情绪如何在语言中表现出来。自然语言处理的一个重要观察结果是,即使没有明确提及情感名称,也可以通过单独参考事件来隐式传达情绪。在心理学中,被称为评估理论的情感理论类别旨在解释事件与情感之间的联系。评估可以被形式化为变量,通过他们认为相关的事件的人们的认知评估来衡量认知评估。其中包括评估事件是否是新颖的,如果该人认为自己负责,是否与自己的目标以及许多其他人保持一致。这样的评估解释了哪些情绪是基于事件开发的,例如,新颖的情况会引起惊喜或不确定后果的人可能引起恐惧。我们在文本中分析了评估理论对情绪分析的适用性,目的是理解注释者是否可以可靠地重建评估概念,如果可以通过文本分类器预测,以及评估概念是否有助于识别情感类别。为了实现这一目标,我们通过要求人们发短信描述触发特定情绪并披露其评估的事件来编译语料库。然后,我们要求读者重建文本中的情感和评估。这种设置使我们能够衡量是否可以纯粹从文本中恢复情绪和评估,并为判断模型的绩效指标提供人体基准。我们将文本分类方法与人类注释者的比较表明,两者都可以可靠地检测出具有相似性能的情绪和评估。我们进一步表明,评估概念改善了文本中情绪的分类。
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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。
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Pragmatics is an essential part of communication, but it remains unclear what mechanisms underlie human pragmatic communication and whether NLP systems capture pragmatic language understanding. To investigate both these questions, we perform a fine-grained comparison of language models and humans on seven pragmatic phenomena, using zero-shot prompting on an expert-curated set of English materials. We ask whether models (1) select pragmatic interpretations of speaker utterances, (2) make similar error patterns as humans, and (3) use similar linguistic cues as humans to solve the tasks. We find that the largest models achieve high accuracy and match human error patterns: within incorrect responses, models favor the literal interpretation of an utterance over heuristic-based distractors. We also find evidence that models and humans are sensitive to similar linguistic cues. Our results suggest that even paradigmatic pragmatic phenomena may be solved without explicit representations of other agents' mental states, and that artificial models can be used to gain mechanistic insights into human pragmatic processing.
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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。
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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。
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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.
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Children acquiring English make systematic errors on subject control sentences even after they have reached near-adult competence (C. Chomsky, 1969), possibly due to heuristics based on semantic roles (Maratsos, 1974). Given the advanced fluency of large generative language models, we ask whether model outputs are consistent with these heuristics, and to what degree different models are consistent with each other. We find that models can be categorized by behavior into three separate groups, with broad differences between the groups. The outputs of models in the largest group are consistent with positional heuristics that succeed on subject control but fail on object control. This result is surprising, given that object control is orders of magnitude more frequent in the text data used to train such models. We examine to what degree the models are sensitive to prompting with agent-patient information, finding that raising the salience of agent and patient relations results in significant changes in the outputs of most models. Based on this observation, we leverage an existing dataset of semantic proto-role annotations (White, et al. 2020) to explore the connections between control and labeling event participants with properties typically associated with agents and patients.
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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。
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As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, and so we refer to the method as 'Constitutional AI'. The process involves both a supervised learning and a reinforcement learning phase. In the supervised phase we sample from an initial model, then generate self-critiques and revisions, and then finetune the original model on revised responses. In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences. We then train with RL using the preference model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a result we are able to train a harmless but non-evasive AI assistant that engages with harmful queries by explaining its objections to them. Both the SL and RL methods can leverage chain-of-thought style reasoning to improve the human-judged performance and transparency of AI decision making. These methods make it possible to control AI behavior more precisely and with far fewer human labels.
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基于变压器的语言模型最近在许多自然语言任务中取得了显着的结果。但是,通常通过利用大量培训数据来实现排行榜的性能,并且很少通过将明确的语言知识编码为神经模型。这使许多人质疑语言学对现代自然语言处理的相关性。在本文中,我介绍了几个案例研究,以说明理论语言学和神经语言模型仍然相互关联。首先,语言模型通过提供一个客观的工具来测量语义距离,这对语言学家很有用,语义距离很难使用传统方法。另一方面,语言理论通过提供框架和数据源来探究我们的语言模型,以了解语言理解的特定方面,从而有助于语言建模研究。本论文贡献了三项研究,探讨了语言模型中语法 - 听觉界面的不同方面。在论文的第一部分中,我将语言模型应用于单词类灵活性的问题。我将Mbert作为语义距离测量的来源,我提供了有利于将单词类灵活性分析为方向过程的证据。在论文的第二部分中,我提出了一种方法来测量语言模型中间层的惊奇方法。我的实验表明,包含形态句法异常的句子触发了语言模型早期的惊喜,而不是语义和常识异常。最后,在论文的第三部分中,我适应了一些心理语言学研究,以表明语言模型包含了论证结构结构的知识。总而言之,我的论文在自然语言处理,语言理论和心理语言学之间建立了新的联系,以为语言模型的解释提供新的观点。
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\ EMPH {人工智能}(AI)系统越来越多地参与影响我们生活的决策,确保自动决策是公平的,道德已经成为最优先事项。直观地,我们觉得类似人的决定,人工代理人的判断应该必然地以一些道德原则为基础。然而,如果有关决定所基础的所有有关因素的全部信息,可以真正伦理(人类或人为)和公平(根据任何道德理论)和公平(根据公平的任何概念)的规定在决策时。这提出了两个问题:(1)在设置中,我们依赖使用通过监督学习获得的分类器的AI系统,存在一些感应/泛化,即使在学习期间也可能不存在一些相关属性。 (2)根据游戏揭示任何 - 无论是道德的纯策略都不可避免地易于剥削,建模这些决定。此外,在许多游戏中,只能通过使用混合策略来获得纳什均衡,即实现数学上最佳结果,决定必须随机化。在本文中,我们认为,在监督学习设置中,存在至少以及确定性分类器的随机分类器,因此在许多情况下可能是最佳选择。我们支持我们的理论效果,具有一个实证研究,表明对随机人工决策者的积极社会态度,并讨论了与使用与当前的AI政策和标准化举措相关的随机分类器相关的一些政策和实施问题。
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