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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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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Human evaluation is the foundation upon which the evaluation of both summarization systems and automatic metrics rests. However, existing human evaluation protocols and benchmarks for summarization either exhibit low inter-annotator agreement or lack the scale needed to draw statistically significant conclusions, and an in-depth analysis of human evaluation is lacking. In this work, we address the shortcomings of existing summarization evaluation along the following axes: 1) We propose a modified summarization salience protocol, Atomic Content Units (ACUs), which relies on fine-grained semantic units and allows for high inter-annotator agreement. 2) We curate the Robust Summarization Evaluation (RoSE) benchmark, a large human evaluation dataset consisting of over 22k summary-level annotations over state-of-the-art systems on three datasets. 3) We compare our ACU protocol with three other human evaluation protocols, underscoring potential confounding factors in evaluation setups. 4) We evaluate existing automatic metrics using the collected human annotations across evaluation protocols and demonstrate how our benchmark leads to more statistically stable and significant results. Furthermore, our findings have important implications for evaluating large language models (LLMs), as we show that LLMs adjusted by human feedback (e.g., GPT-3.5) may overfit unconstrained human evaluation, which is affected by the annotators' prior, input-agnostic preferences, calling for more robust, targeted evaluation methods.
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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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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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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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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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A primary objective of news articles is to establish the factual record for an event, frequently achieved by conveying both the details of the specified event (i.e., the 5 Ws; Who, What, Where, When and Why regarding the event) and how people reacted to it (i.e., reported statements). However, existing work on news summarization almost exclusively focuses on the event details. In this work, we propose the novel task of summarizing the reactions of different speakers, as expressed by their reported statements, to a given event. To this end, we create a new multi-document summarization benchmark, SUMREN, comprising 745 summaries of reported statements from various public figures obtained from 633 news articles discussing 132 events. We propose an automatic silver training data generation approach for our task, which helps smaller models like BART achieve GPT-3 level performance on this task. Finally, we introduce a pipeline-based framework for summarizing reported speech, which we empirically show to generate summaries that are more abstractive and factual than baseline query-focused summarization approaches.
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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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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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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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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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诸如学术文章和商业报告之类的长期文件一直是详细说明重要问题和需要额外关注的复杂主题的标准格式。自动汇总系统可以有效地将长文档置于简短而简洁的文本中,以封装最重要的信息,从而在帮助读者的理解中很重要。最近,随着神经体系结构的出现,已经做出了重大的研究工作,以推动自动文本摘要系统,以及有关将这些系统扩展到长期文档领域的挑战的大量研究。在这项调查中,我们提供了有关长期文档摘要的研究的全面概述,以及其研究环境的三个主要组成部分的系统评估:基准数据集,汇总模型和评估指标。对于每个组成部分,我们在长期汇总的背景下组织文献,并进行经验分析,以扩大有关当前研究进度的观点。实证分析包括一项研究基准数据集的内在特征,摘要模型的多维分析以及摘要评估指标的综述。根据总体发现,我们通过提出可能在这个快速增长的领域中提出未来探索的方向来得出结论。
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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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The proliferation of automatic faithfulness metrics for summarization has produced a need for benchmarks to evaluate them. While existing benchmarks measure the correlation with human judgements of faithfulness on model-generated summaries, they are insufficient for diagnosing whether metrics are: 1) consistent, i.e., decrease as errors are introduced into a summary, 2) effective on human-written texts, and 3) sensitive to different error types (as summaries can contain multiple errors). To address these needs, we present a benchmark of unfaithful minimal pairs (BUMP), a dataset of 889 human-written, minimally different summary pairs, where a single error (from an ontology of 7 types) is introduced to a summary from the CNN/DailyMail dataset to produce an unfaithful summary. We find BUMP complements existing benchmarks in a number of ways: 1) the summaries in BUMP are harder to discriminate and less probable under SOTA summarization models, 2) BUMP enables measuring the consistency of metrics, and reveals that the most discriminative metrics tend not to be the most consistent, 3) BUMP enables the measurement of metrics' performance on individual error types and highlights areas of weakness for future work.
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Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog. However, they sometimes generate unsupported or misleading content. A user cannot easily determine whether their outputs are trustworthy or not, because most LMs do not have any built-in mechanism for attribution to external evidence. To enable attribution while still preserving all the powerful advantages of recent generation models, we propose RARR (Retrofit Attribution using Research and Revision), a system that 1) automatically finds attribution for the output of any text generation model and 2) post-edits the output to fix unsupported content while preserving the original output as much as possible. When applied to the output of several state-of-the-art LMs on a diverse set of generation tasks, we find that RARR significantly improves attribution while otherwise preserving the original input to a much greater degree than previously explored edit models. Furthermore, the implementation of RARR requires only a handful of training examples, a large language model, and standard web search.
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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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我们调整了大型语言模型,以使用行为克隆来编写自然语言批评(自然语言批判性评论)。关于基于主题的摘要任务,我们的模型所写的批评帮助人类在摘要中发现了本来会错过的漏洞。我们的模型有助于在模型和人类书面摘要中发现自然存在的缺陷,以及人类撰写的摘要中有意误导的摘要中的缺陷。我们研究批评的缩放特性,包括基于主题的汇总和合成任务。较大的模型写出更多有用的批评,在大多数任务上,尽管产生了更困难的输出,但在大多数任务上都更好地进行了自我关注。较大的模型还可以将自己的自我批评纳入反馈,将自己的摘要完善为更好的摘要。最后,我们激励并引入了一个框架,以比较批评能力的产生和歧视能力。我们的测量表明,即使是大型模型也可能仍然具有他们无法或不表达为批评的相关知识。这些结果是使用AI辅助的人类反馈来扩展机器学习系统的监督到人类直接评估的任务的概念证明。我们释放培训数据集以及批评援助实验的样本。
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我们微调GPT-3使用基于文本的Web浏览环境来回答长形问题,允许模型搜索和导航Web。通过建立任务,以便通过人类执行,我们能够使用模仿学习培训在任务上的模型,然后通过人体反馈优化答案质量。为了使人为评估事实精度更容易,模型必须在浏览支持答案时收集引用。我们在ELI5上培训并评估我们的模型,Reddit用户提出的问题数据集。我们的最佳模型是通过使用行为克隆进行微调GPT-3获得的,然后对训练训练的奖励模型进行拒绝采样来获得以预测人类偏好。这种模式的答案是人类56%的答案,我们的人类示威者的时间和69%的时间到Reddit的最高投票答复。
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