In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark, introduced a little over one year ago, offers a single-number metric that summarizes progress on a diverse set of such tasks, but performance on the benchmark has recently surpassed the level of non-expert humans, suggesting limited headroom for further research. In this paper we present SuperGLUE, a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard. SuperGLUE is available at super.gluebenchmark.com.
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Winograd架构挑战 - 一套涉及代词参考消歧的双句话,似乎需要使用致辞知识 - 是由2011年的赫克托勒维克斯提出的。到2019年,基于大型预先训练的变压器的一些AI系统基于语言模型和微调这些问题,精度优于90%。在本文中,我们审查了Winograd架构挑战的历史并评估了其重要性。
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为了实现长文档理解的构建和测试模型,我们引入质量,具有中文段的多项选择QA DataSet,具有约5,000个令牌的平均长度,比典型的当前模型更长。与经过段落的事先工作不同,我们的问题是由阅读整个段落的贡献者编写和验证的,而不是依赖摘要或摘录。此外,只有一半的问题是通过在紧缩时间限制下工作的注释器来应答,表明略读和简单的搜索不足以一直表现良好。目前的模型在此任务上表现不佳(55.4%),并且落后于人类性能(93.5%)。
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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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For natural language understanding (NLU) technology to be maximally useful, it must be able to process language in a way that is not exclusive to a single task, genre, or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation (GLUE) benchmark, a collection of tools for evaluating the performance of models across a diverse set of existing NLU tasks. By including tasks with limited training data, GLUE is designed to favor and encourage models that share general linguistic knowledge across tasks. GLUE also includes a hand-crafted diagnostic test suite that enables detailed linguistic analysis of models. We evaluate baselines based on current methods for transfer and representation learning and find that multi-task training on all tasks performs better than training a separate model per task. However, the low absolute performance of our best model indicates the need for improved general NLU systems.
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大型预先训练的语言模型已经显示了几次拍摄学习的承诺,只提供了几个任务特定示例给出了基于文本的任务。款式将很快解决到目前为止为人类研究助理保留的分类任务吗?现有的基准标记不设计用于衡量应用设置的进度,因此不要直接回答这个问题。 RAFT基准(现实世界注释的少量拍摄任务)侧重于自然发生的任务,并使用镜像部署的评估设置。 RAFT的基线评估揭示了当前技术斗争的地区:推理在许多班级的长篇文章和任务上。人类基线表明,非专家人类难以反映出一些分类任务,反映了现实世界的价值有时依赖于域名专业知识。甚至非专业人类基线F1分数超过GPT-3平均为0.11。 RAFT DataSets和排行榜将跟踪哪些模型改进在https://raft.elict.org中转化为现实世界的优势。
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有关应答数据集和模型的研究在研究界中获得了很多关注。其中许多人释放了自己的问题应答数据集以及模型。我们在该研究领域看到了巨大的进展。本调查的目的是识别,总结和分析许多研究人员释放的现有数据集,尤其是在非英语数据集以及研究代码和评估指标等资源中。在本文中,我们审查了问题应答数据集,这些数据集可以以法语,德语,日语,中文,阿拉伯语,俄语以及多语言和交叉的问答数据集进行英语。
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Visual understanding goes well beyond object recognition. With one glance at an image, we can effortlessly imagine the world beyond the pixels: for instance, we can infer people's actions, goals, and mental states. While this task is easy for humans, it is tremendously difficult for today's vision systems, requiring higher-order cognition and commonsense reasoning about the world. We formalize this task as Visual Commonsense Reasoning. Given a challenging question about an image, a machine must answer correctly and then provide a rationale justifying its answer.Next, we introduce a new dataset, VCR, consisting of 290k multiple choice QA problems derived from 110k movie scenes. The key recipe for generating non-trivial and highquality problems at scale is Adversarial Matching, a new approach to transform rich annotations into multiple choice questions with minimal bias. Experimental results show that while humans find VCR easy (over 90% accuracy), state-of-the-art vision models struggle (∼45%).To move towards cognition-level understanding, we present a new reasoning engine, Recognition to Cognition Networks (R2C), that models the necessary layered inferences for grounding, contextualization, and reasoning. R2C helps narrow the gap between humans and machines (∼65%); still, the challenge is far from solved, and we provide analysis that suggests avenues for future work.
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Humans gather information through conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational questions. We introduce CoQA, a novel dataset for building Conversational Question Answering systems. 1 Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets, e.g., coreference and pragmatic reasoning. We evaluate strong dialogue and reading comprehension models on CoQA. The best system obtains an F1 score of 65.4%, which is 23.4 points behind human performance (88.8%), indicating there is ample room for improvement. We present CoQA as a challenge to the community at https://stanfordnlp. github.io/coqa.
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This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. At 433k examples, this resource is one of the largest corpora available for natural language inference (a.k.a. recognizing textual entailment), improving upon available resources in both its coverage and difficulty. MultiNLI accomplishes this by offering data from ten distinct genres of written and spoken English, making it possible to evaluate systems on nearly the full complexity of the language, while supplying an explicit setting for evaluating cross-genre domain adaptation. In addition, an evaluation using existing machine learning models designed for the Stanford NLI corpus shows that it represents a substantially more difficult task than does that corpus, despite the two showing similar levels of inter-annotator agreement. This task will involve reading a line from a non-fiction article and writing three sentences that relate to it. The line will describe a situation or event. Using only this description and what you know about the world:
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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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自Bert(Devlin等,2018)以来,学习上下文化的单词嵌入一直是NLP中的事实上的标准。然而,学习上下文化短语嵌入的进展受到缺乏人类通知的语句基准基准的阻碍。为了填补这一空白,我们提出了PIC- 〜28K名词短语的数据集伴随着它们的上下文Wikipedia页面,以及一套三个任务,这些任务增加了评估短语嵌入质量的难度。我们发现,在我们的数据集中进行的培训提高了排名模型的准确性,并明显地将问题答案(QA)模型推向了近人类的准确性,而在语义搜索上,鉴于询问短语和段落,在语义搜索上是95%的精确匹配(EM)。有趣的是,我们发现这种令人印象深刻的性能的证据是因为质量检查模型学会了更好地捕获短语的共同含义,而不管其实际背景如何。也就是说,在我们的短语中歧义歧义(PSD)任务上,SOTA模型的精度大大下降(60%EM),在两个不同情况下未能区分相同短语的两种不同感觉。在我们的3任任务基准测试中的进一步结果表明,学习上下文化的短语嵌入仍然是一个有趣的开放挑战。
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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.
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培训和评估语言模型越来越多地要求构建元数据 - 多样化的策划数据收集,并具有清晰的出处。自然语言提示最近通过将现有的,有监督的数据集转换为多种新颖的预处理任务,突出了元数据策划的好处,从而改善了零击的概括。尽管将这些以数据为中心的方法转化为生物医学语言建模的通用域文本成功,但由于标记的生物医学数据集在流行的数据中心中的代表性大大不足,因此仍然具有挑战性。为了应对这一挑战,我们介绍了BigBio一个由126个以上的生物医学NLP数据集的社区库,目前涵盖12个任务类别和10多种语言。 BigBio通过对数据集及其元数据进行程序化访问来促进可再现的元数据策划,并与当前的平台兼容,以及时工程和端到端的几个/零射击语言模型评估。我们讨论了我们的任务架构协调,数据审核,贡献指南的过程,并概述了两个说明性用例:生物医学提示和大规模,多任务学习的零射门评估。 BigBio是一项持续的社区努力,可在https://github.com/bigscience-workshop/biomedical上获得。
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Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few-shot generalization to unseen tasks. However, there is a limited understanding of the performance trade-offs of different decisions made during the instruction-tuning process. These decisions include the scale and diversity of the instruction-tuning benchmark, different task sampling strategies, fine-tuning with and without demonstrations, training using specialized datasets for reasoning and dialogue, and finally, the fine-tuning objectives themselves. In this paper, we characterize the effect of instruction-tuning decisions on downstream task performance when scaling both model and benchmark sizes. To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks. Through the lens of this framework, we first present insights about instruction-tuning decisions as applied to OPT-30B and further exploit these insights to train OPT-IML 30B and 175B, which are instruction-tuned versions of OPT. OPT-IML demonstrates all three generalization abilities at both scales on four different evaluation benchmarks with diverse tasks and input formats -- PromptSource, FLAN, Super-NaturalInstructions, and UnifiedSKG. Not only does it significantly outperform OPT on all benchmarks but is also highly competitive with existing models fine-tuned on each specific benchmark. We release OPT-IML at both scales, together with the OPT-IML Bench evaluation framework.
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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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众包NLP数据集的反复挑战是,在制作示例时,人类作家通常会依靠重复的模式,从而导致缺乏语言多样性。我们介绍了一种基于工人和AI协作的数据集创建的新方法,该方法汇集了语言模型的生成力量和人类的评估力量。从现有的数据集,自然语言推理(NLI)的Multinli开始,我们的方法使用数据集制图自动识别示例来证明具有挑战性的推理模式,并指示GPT-3撰写具有相似模式的新示例。然后,机器生成的示例会自动过滤,并最终由人类人群工人修订和标记。最终的数据集Wanli由107,885个NLI示例组成,并在现有的NLI数据集上呈现出独特的经验优势。值得注意的是,培训有关Wanli的模型,而不是Multinli($ 4 $ $倍)可改善我们考虑的七个外域测试集的性能,包括汉斯(Hans)的11%和对抗性NLI的9%。此外,将Multinli与Wanli结合起来比将其与其他NLI增强集相结合更有效。我们的结果表明,自然语言生成技术的潜力是策划增强质量和多样性的NLP数据集。
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随着近期自然语言生成(NLG)模型的各种应用程序的改进,它变得必须具有识别和评估NLG输出是否仅共享关于外部世界的可验证信息的手段。在这项工作中,我们提出了一个归属于识别的来源(AIS)的新评估框架,用于评估自然语言生成模型的输出,当这种输出涉及外部世界时。我们首先定义AIS,并引入两级注释管道,用于允许注释器根据AIS指南适当地评估模型输出。通过人为评估研究,我们在三个代数据集(会话QA域中的两个中和总结一下,概括地验证了这种方法,表明AIS可以作为测量模型生成的语句是否支持基础来源的常见框架。我们释放人类评估研究指南。
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数据增强是自然语言处理(NLP)模型的鲁棒性评估的重要组成部分,以及增强他们培训的数据的多样性。在本文中,我们呈现NL-Cogmenter,这是一种新的参与式Python的自然语言增强框架,它支持创建两个转换(对数据的修改)和过滤器(根据特定功能的数据拆分)。我们描述了框架和初始的117个变换和23个过滤器,用于各种自然语言任务。我们通过使用其几个转换来分析流行自然语言模型的鲁棒性来证明NL-Upmenter的功效。基础架构,Datacards和稳健性分析结果在NL-Augmenter存储库上公开可用(\ url {https://github.com/gem-benchmark/nl-augmenter})。
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相同上下文的可能后果可能会因我们所指的情况而异。但是,当前在自然语言处理中的研究并不集中于多种可能情况下的常识性推理。本研究通过短篇小说文字提出与候选人答案相同的结尾的多个问题来构成这项任务。我们由此产生的数据集,可能的故事,包括超过1.3k的故事文本超过4.5k的问题。我们发现,即使是目前的强训练性语言模型也很难始终如一地回答问题,这强调了无监督环境中最高的准确性(60.2%)远远落后于人类准确性(92.5%)。通过与现有数据集进行比较,我们观察到数据集中的问题包含答案选项中的最小注释伪像。此外,我们的数据集还包括需要反事实推理的示例,以及需要读者的反应和虚构信息的示例,这表明我们的数据集可以作为对未来常识性推理的未来研究的挑战性测试。
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