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.
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现代神经语言模型广泛用于任务中的任务,跨越培训数据记忆敏感信息。由于模型继续扩大参数,培训数据和计算,从学习理论的角度来看,培训数据和计算中的记忆既重要性也很重要,并且在现实世界应用中实际上至关重要。在语言模型中记忆的研究中的一个开放问题是如何过滤掉“常见的”记忆。事实上,大多数记忆标准与培训集的出现数量强烈关联,捕获“常见”记忆,例如熟悉的短语,公共知识或模板文本。在本文中,我们提供了由心理学中人类记忆分类的理性观点。从这个角度来看,我们制定了反事实记忆的概念,这表征了模型的预测如何改变,如果在训练期间省略了特定文件。我们在标准文本数据集中识别并研究了反复记忆培训示例。我们进一步估计每个训练示例对验证集和生成文本的影响,并显示这可以提供在测试时间的记忆源的直接证据。
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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.
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讨论了与科学,工程,建筑和人为因素相关的月球表面上的运输设施问题。未来十年制造的后勤决策可能对财务成功至关重要。除了概述一些问题及其与数学和计算的关系外,本文还为决策者,科学家和工程师提供了有用的资源。
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Pretrained transformer models have achieved state-of-the-art results in many tasks and benchmarks recently. Many state-of-the-art Language Models (LMs), however, do not scale well above the threshold of 512 input tokens. In specialized domains though (such as legal, scientific or biomedical), models often need to process very long text (sometimes well above 10000 tokens). Even though many efficient transformers have been proposed (such as Longformer, BigBird or FNet), so far, only very few such efficient models are available for specialized domains. Additionally, since the pretraining process is extremely costly in general - but even more so as the sequence length increases - it is often only in reach of large research labs. One way of making pretraining cheaper is the Replaced Token Detection (RTD) task, by providing more signal during training, since the loss can be computed over all tokens. In this work, we train Longformer models with the efficient RTD task on legal data to showcase that pretraining efficient LMs is possible using much less compute. We evaluate the trained models on challenging summarization tasks requiring the model to summarize long texts to show to what extent the models can achieve good performance on downstream tasks. We find that both the small and base models outperform their baselines on the in-domain BillSum and out-of-domain PubMed tasks in their respective parameter range. We publish our code and models for research purposes.
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Alphazero,Leela Chess Zero和Stockfish Nnue革新了计算机国际象棋。本书对此类引擎的技术内部工作进行了完整的介绍。该书分为四个主要章节 - 不包括第1章(简介)和第6章(结论):第2章引入神经网络,涵盖了所有用于构建深层网络的基本构建块,例如Alphazero使用的网络。内容包括感知器,后传播和梯度下降,分类,回归,多层感知器,矢量化技术,卷积网络,挤压网络,挤压和激发网络,完全连接的网络,批处理归一化和横向归一化和跨性线性单位,残留层,剩余层,过度效果和底漆。第3章介绍了用于国际象棋发动机以及Alphazero使用的经典搜索技术。内容包括minimax,alpha-beta搜索和蒙特卡洛树搜索。第4章展示了现代国际象棋发动机的设计。除了开创性的Alphago,Alphago Zero和Alphazero我们涵盖Leela Chess Zero,Fat Fritz,Fat Fritz 2以及有效更新的神经网络(NNUE)以及MAIA。第5章是关于实施微型α。 Shexapawn是国际象棋的简约版本,被用作为此的示例。 Minimax搜索可以解决六ap峰,并产生了监督学习的培训位置。然后,作为比较,实施了类似Alphazero的训练回路,其中通过自我游戏进行训练与强化学习结合在一起。最后,比较了类似α的培训和监督培训。
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The majority of available text summarization datasets include short-form source documents that lack long-range causal and temporal dependencies, and often contain strong layout and stylistic biases. While relevant, such datasets will offer limited challenges for future generations of text summarization systems. We address these issues by introducing BookSum, a collection of datasets for long-form narrative summarization. Our dataset covers source documents from the literature domain, such as novels, plays and stories, and includes highly abstractive, human written summaries on three levels of granularity of increasing difficulty: paragraph-, chapter-, and book-level. The domain and structure of our dataset poses a unique set of challenges for summarization systems, which include: processing very long documents, non-trivial causal and temporal dependencies, and rich discourse structures. To facilitate future work, we trained and evaluated multiple extractive and abstractive summarization models as baselines for our dataset.
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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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GPT-3等模型的零和少量提示的最新成功导致了NLP研究的范式转移。在本文中,我们研究了其对文本摘要的影响,重点是新闻摘要的经典基准领域。首先,我们研究了零击GPT-3与在大型摘要数据集中训练的微调模型的比较。我们表明,不仅人类压倒性地更喜欢GPT-3摘要,而且这些摘要也不遭受普通数据集特异性问题(例如事实差的问题)。接下来,我们研究这对评估意味着什么,尤其是黄金标准测试集的作用。我们的实验表明,基于参考和无参考的自动指标,例如最近提出的基于质量检查或基于质量的事实方法无法可靠地评估零击摘要。最后,我们讨论了未来的研究挑战,除了通用摘要之外,特别是基于关键字和方面的摘要,表明了优势微调方法与零拍的提示相比如何。为了支持进一步的研究,我们发布:(a)在4个标准摘要基准中,从微调和零摄像模型中产生的10K生成的摘要,(b)1K人类偏好判断和比较不同系统的普通系统,以进行通用和关键字的不同系统。基于摘要。
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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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Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset -matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations.
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自动化驾驶系统(广告)开辟了汽车行业的新领域,为未来的运输提供了更高的效率和舒适体验的新可能性。然而,在恶劣天气条件下的自主驾驶已经存在,使自动车辆(AVS)长时间保持自主车辆(AVS)或更高的自主权。本文评估了天气在分析和统计方式中为广告传感器带来的影响和挑战,并对恶劣天气条件进行了解决方案。彻底报道了关于对每种天气的感知增强的最先进技术。外部辅助解决方案如V2X技术,当前可用的数据集,模拟器和天气腔室的实验设施中的天气条件覆盖范围明显。通过指出各种主要天气问题,自主驾驶场目前正在面临,近年来审查硬件和计算机科学解决方案,这项调查概述了在不利的天气驾驶条件方面的障碍和方向的障碍和方向。
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Advocates of algorithmic techniques like data mining argue that these techniques eliminate human biases from the decision-making process. But an algorithm is only as good as the data it works with. Data is frequently imperfect in ways that allow these algorithms to inherit the prejudices of prior decision makers. In other cases, data may simply reflect the widespread biases that persist in society at large. In still others, data mining can discover surprisingly useful regularities that are really just preexisting patterns of exclusion and inequality. Unthinking reliance on data mining can deny historically disadvantaged and vulnerable groups full participation in society. Worse still, because the resulting discrimination is almost always an unintentional emergent property of the algorithm's use rather than a conscious choice by its programmers, it can be unusually hard to identify the source of the problem or to explain it to a court. This Essay examines these concerns through the lens of American antidiscrimination law-more particularly, through Title
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Transfer learning, where a model is first pre-trained on a data-rich task before being finetuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.
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本次调查绘制了用于分析社交媒体数据的生成方法的研究状态的广泛的全景照片(Sota)。它填补了空白,因为现有的调查文章在其范围内或被约会。我们包括两个重要方面,目前正在挖掘和建模社交媒体的重要性:动态和网络。社会动态对于了解影响影响或疾病的传播,友谊的形成,友谊的形成等,另一方面,可以捕获各种复杂关系,提供额外的洞察力和识别否则将不会被注意的重要模式。
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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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信号处理是几乎任何传感器系统的基本组件,具有不同科学学科的广泛应用。时间序列数据,图像和视频序列包括可以增强和分析信息提取和量化的代表性形式的信号。人工智能和机器学习的最近进步正在转向智能,数据驱动,信号处理的研究。该路线图呈现了最先进的方法和应用程序的关键概述,旨在突出未来的挑战和对下一代测量系统的研究机会。它涵盖了广泛的主题,从基础到工业研究,以简明的主题部分组织,反映了每个研究领域的当前和未来发展的趋势和影响。此外,它为研究人员和资助机构提供了识别新前景的指导。
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随着全球人口越来越多的人口驱动世界各地的快速城市化,有很大的需要蓄意审议值得生活的未来。特别是,随着现代智能城市拥抱越来越多的数据驱动的人工智能服务,值得记住技术可以促进繁荣,福祉,城市居住能力或社会正义,而是只有当它具有正确的模拟补充时(例如竭尽全力,成熟机构,负责任治理);这些智能城市的最终目标是促进和提高人类福利和社会繁荣。研究人员表明,各种技术商业模式和特征实际上可以有助于极端主义,极化,错误信息和互联网成瘾等社会问题。鉴于这些观察,解决了确保了诸如未来城市技术基岩的安全,安全和可解释性的哲学和道德问题,以为未来城市的技术基岩具有至关重要的。在全球范围内,有能够更加人性化和以人为本的技术。在本文中,我们分析和探索了在人以人为本的应用中成功部署AI的安全,鲁棒性,可解释性和道德(数据和算法)挑战的关键挑战,特别强调这些概念/挑战的融合。我们对这些关键挑战提供了对现有文献的详细审查,并分析了这些挑战中的一个可能导致他人的挑战方式或帮助解决其他挑战。本文还建议了这些域的当前限制,陷阱和未来研究方向,以及如何填补当前的空白并导致更好的解决方案。我们认为,这种严谨的分析将为域名的未来研究提供基准。
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随着大型语言模型的出现,抽象性摘要的方法取得了长足的进步,从而在应用程序中使用了帮助知识工人处理笨拙的文档收集的潜力。一个这样的环境是民权诉讼交换所(CRLC)(https://clearinghouse.net),其中发布了有关大规模民权诉讼,服务律师,学者和公众的信息。如今,CRLC中的摘要需要对律师和法律专业的学生进行广泛的培训,这些律师和法律专业的学生花费数小时了解多个相关文件,以便产生重要事件和结果的高质量摘要。在这种持续的现实世界摘要工作的激励下,我们引入了Multi-iplesum,这是由正在进行的CRLC写作中绘制的9,280个专家作者的摘要集。鉴于源文档的长度,多文章介绍了一个具有挑战性的多文档摘要任务,通常每个情况超过200页。此外,多胎sum与其多个目标摘要中的其他数据集不同,每个数据集都处于不同的粒度(从一句“极端”摘要到超过五百个单词的多段落叙述)。我们提供了广泛的分析,表明,尽管培训数据(遵守严格的内容和样式准则)中的摘要很高,但最新的摘要模型在此任务上的表现较差。我们发布了多体式的摘要方法,以及促进应用程序的开发,以协助CRLC的任务https://multilexsum.github.io。
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本文档提供了SNACS的详细语言描述(Adposition和Case Supersenses的语义网络; Schneider等,2018),这是52个语义标签(“ Supersenses”)的库存,这些标签(“ Supersenses”)表征了在某种程度上使用ADIP定位和案例标记的使用。粒度水平,如Streusle语料库中所示(https://github.com/nert-nlp/streusle/;版本4.5 track track track offelines guidelines guidelines版本2.6)。尽管SNACS的库存渴望成为普遍的,但该文档是特定于英语的。其他语言的文档将单独发布。版本2是Schneider等人对英语提出的超音库存的修订。 (2015,2016)(此后为“ V1”),这又基于以前的计划。本清单是在对英语的V1语料库注释进行广泛审查后开发的,以及以前未分析的属格案例所有人(Blodgett和Schneider,2018年),并考虑了希伯来语,印地语,韩国和德国的定义和案例现象的考虑。 Hwang等。 (2017)介绍了V2方案的理论基础。 Schneider等。 (2018)总结了该方案,其应用于英语语料库数据以及自动歧义任务。刘等。 (2021)提供了一个英语词法语义识别标签仪,其中包括SNACS标签的输出。该文档也可以与Xposition网站上的语料库数据一起浏览(Gessler等,2022):http://www.xposition.org/
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