Despite the recent progress in language generation models, their outputs may not always meet user expectations. In this work, we study whether informational feedback in natural language can be leveraged to improve generation quality and user preference alignment. To this end, we consider factual consistency in summarization, the quality that the summary should only contain information supported by the input documents, for user preference alignment. We collect a high-quality dataset, DeFacto, containing human demonstrations and informational feedback in natural language consisting of corrective instructions, edited summaries, and explanations with respect to the factual consistency of the summary. Using our dataset, we study two natural language generation tasks: 1) editing a summary using the human feedback, and 2) generating human feedback from the original summary. Using the two tasks, we further evaluate if models can automatically correct factual inconsistencies in generated summaries. We show that the human-edited summaries we collected are more factually consistent, and pre-trained language models can leverage our dataset to improve the factual consistency of original system-generated summaries in our proposed generation tasks. We make the DeFacto dataset publicly available at https://github.com/microsoft/DeFacto.

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.

文本内容通常是协作写作过程的输出：我们从初始草稿开始，提出建议并反复进行更改。不可知的是，当今的语言模型只能产生最终结果。结果，他们缺乏对协作写作至关重要的几种能力：他们无法更新现有文本，难以控制和无法进行口头计划或解释其行为。为了解决这些缺点，我们介绍了Peer，这是一种协作语言模型，经过训练以模仿整个写作过程本身：Peer可以编写草稿，添加建议，提出编辑并为其行为提供解释。至关重要的是，我们训练多个同伴能够填补写作过程的各个部分的实例，从而可以使用自训练技术来提高培训数据的质量，数量和多样性。这通过使其适用于没有编辑历史的域，并提高其遵循说明，编写有用的评论并解释其动作的能力，从而释放了Peer的全部潜力。我们表明，同行在各个领域和编辑任务上取得了强大的性能。

Current metrics for evaluating factuality for abstractive document summarization have achieved high correlations with human judgment, but they do not account for the vision modality and thus are not adequate for vision-and-language summarization. We propose CLIPBERTScore, a simple weighted combination of CLIPScore and BERTScore to leverage the robustness and strong factuality detection performance between image-summary and document-summary, respectively. Next, due to the lack of meta-evaluation benchmarks to evaluate the quality of multimodal factuality metrics, we collect human judgments of factuality with respect to documents and images. We show that this simple combination of two metrics in the zero-shot setting achieves higher correlations than existing factuality metrics for document summarization, outperforms an existing multimodal summarization metric, and performs competitively with strong multimodal factuality metrics specifically fine-tuned for the task. Our thorough analysis demonstrates the robustness and high correlation of CLIPBERTScore and its components on four factuality metric-evaluation benchmarks. Finally, we demonstrate two practical downstream applications of our CLIPBERTScore metric: for selecting important images to focus on during training, and as a reward for reinforcement learning to improve factuality of multimodal summary generation w.r.t automatic and human evaluation. Our data and code are publicly available at https://github.com/meetdavidwan/faithful-multimodal-summ

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。

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.

迄今为止对文本生成的评估主要集中在依次创建的内容上，而不是对文本的改进。但是，写作自然是一个迭代和增量过程，需要在不同的模块化技能上进行专业知识，例如修复过时的信息或使样式更加一致。即便如此，对模型执行这些技能和编辑能力的模型能力的全面评估仍然很少。这项工作介绍了EditeVal：基于指导的，基准和评估套件，该套件利用现有的现有和新数据集自动评估编辑功能，例如使文本更具凝聚力和释义。我们评估了几种预训练的模型，这表明指令和同伴表现最好，但是大多数基准都落在监督的SOTA以下，尤其是在中和和更新信息时。我们的分析还表明，用于编辑任务的常用指标并不总是很好地关联，并且对具有最高性能的提示的优化并不一定带来对不同模型的最强鲁棒性。通过发布此基准和公开可用的排行榜挑战，我们希望在开发能够迭代和更可控制的编辑模型中解锁未来的研究。

Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from generating factually inconsistent summaries, reducing their utility for real-world application. Several recent efforts attempt to address this by devising models that automatically detect factual inconsistencies in machine generated summaries. However, they focus exclusively on English, a language with abundant resources. In this work, we leverage factual consistency evaluation models to improve multilingual summarization. We explore two intuitive approaches to mitigate hallucinations based on the signal provided by a multilingual NLI model, namely data filtering and controlled generation. Experimental results in the 45 languages from the XLSum dataset show gains over strong baselines in both automatic and human evaluation.

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。

在摘要域中，摘要的关键要求是与输入文档一致。以前的工作发现，当应用于不一致检测时，自然语言推理（NLI）模型不会竞争地执行。在这项工作中，我们重新访问NLI的使用进行不一致检测，发现过去的工作遭到了NLI数据集（句子级）与不一致检测（文档级别）之间的输入粒度不匹配。我们提供称为SummacConv的高效和轻量级方法，使NLI模型能够通过将文档分段为句子单元并在句子对之间聚合得分来成功地用于此任务。在我们的新推出的基准名为Summac（简介一致性）中由六个大的不一致检测数据集组成，SummacConv以74.4％的均衡精度获得最先进的结果，与现有工作相比，5％的点改进。我们制作可用的模型和数据集：https://github.com/tingofurro/summac

查询聚焦的文本摘要（QFTS）任务旨在构建基于给定查询的文本文档摘要的构建系统。解决此任务的关键挑战是缺乏培训摘要模型的大量标记数据。在本文中，我们通过探索一系列域适应技术来解决这一挑战。鉴于最近在广泛的自然语言处理任务中进行预先接受的变压器模型的成功，我们利用此类模型为单文档和多文件方案的QFTS任务产生抽象摘要。对于域适应，我们使用预先训练的变压器的摘要模型应用了各种技术，包括转移学习，弱监督学习和远程监督。六个数据集的广泛实验表明，我们所提出的方法非常有效地为QFTS任务产生抽象摘要，同时在一组自动和人类评估指标上设置新的最先进的结果。

最先进的抽象摘要系统经常生成\ emph {幻觉};即，不直接从源文本中推断的内容。尽管被认为是不正确的，我们发现非常令人难潮的内容是事实，即与世界知识一致。这些事实幻觉通过提供有用的背景信息，可以在摘要中受益。在这项工作中，我们提出了一种新的检测方法，将事实与实体的非事实幻觉分开。我们的方法分别使用实体的先前和后验概率，分别是预训练和芬特的屏蔽语言模型。经验结果表明，我们的方法在精度和F1分数方面大大优于两种基线％，与人类判断强烈相关。百分比对事实分类任务。此外，我们显示我们的探测器，当用作离线增强学习（RL）算法中的奖励信号时，显着提高了摘要的事实性，同时保持抽象水平。

Instruction tuning enables pretrained language models to perform new tasks from inference-time natural language descriptions. These approaches rely on vast amounts of human supervision in the form of crowdsourced datasets or user interactions. In this work, we introduce Unnatural Instructions: a large dataset of creative and diverse instructions, collected with virtually no human labor. We collect 64,000 examples by prompting a language model with three seed examples of instructions and eliciting a fourth. This set is then expanded by prompting the model to rephrase each instruction, creating a total of approximately 240,000 examples of instructions, inputs, and outputs. Experiments show that despite containing a fair amount of noise, training on Unnatural Instructions rivals the effectiveness of training on open-source manually-curated datasets, surpassing the performance of models such as T0++ and Tk-Instruct across various benchmarks. These results demonstrate the potential of model-generated data as a cost-effective alternative to crowdsourcing for dataset expansion and diversification.

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.

Controllable summarization allows users to generate customized summaries with specified attributes. However, due to the lack of designated annotations of controlled summaries, existing works have to craft pseudo datasets by adapting generic summarization benchmarks. Furthermore, most research focuses on controlling single attributes individually (e.g., a short summary or a highly abstractive summary) rather than controlling a mix of attributes together (e.g., a short and highly abstractive summary). In this paper, we propose MACSum, the first human-annotated summarization dataset for controlling mixed attributes. It contains source texts from two domains, news articles and dialogues, with human-annotated summaries controlled by five designed attributes (Length, Extractiveness, Specificity, Topic, and Speaker). We propose two simple and effective parameter-efficient approaches for the new task of mixed controllable summarization based on hard prompt tuning and soft prefix tuning. Results and analysis demonstrate that hard prompt models yield the best performance on all metrics and human evaluations. However, mixed-attribute control is still challenging for summarization tasks. Our dataset and code are available at https://github.com/psunlpgroup/MACSum.

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.

自动摘要方法是有效的，但可能患有低质量。相比之下，手动摘要很昂贵，但质量更高。人类和人工智能可以协作以提高总结性能吗？在类似的文本生成任务（例如机器翻译）中，人类AI合作的形式是“后编辑” AI生成的文本，可减少人类的工作量并提高AI输出的质量。因此，我们探讨了邮政编辑是否提供文本摘要中的优势。具体来说，我们对72名参与者进行了实验，将提供的后编辑摘要与手动摘要进行了摘要，以摘要质量，人为效率和用户在正式新闻（XSUM新闻）和非正式（REDDIT帖子）文本方面进行了比较。这项研究对何时编辑的文本摘要提供了宝贵的见解：在某些情况下（例如，何时参与者缺乏领域知识），但在其他情况下却没有帮助（例如，何时提供的摘要包括不准确的信息）。参与者的不同编辑策略和援助需求为未来的人类摘要系统提供了影响。

Abstractive dialogue summarization has received increasing attention recently. Despite the fact that most of the current dialogue summarization systems are trained to maximize the likelihood of human-written summaries and have achieved significant results, there is still a huge gap in generating high-quality summaries as determined by humans, such as coherence and faithfulness, partly due to the misalignment in maximizing a single human-written summary. To this end, we propose to incorporate different levels of human feedback into the training process. This will enable us to guide the models to capture the behaviors humans care about for summaries. Specifically, we ask humans to highlight the salient information to be included in summaries to provide the local feedback , and to make overall comparisons among summaries in terms of coherence, accuracy, coverage, concise and overall quality, as the global feedback. We then combine both local and global feedback to fine-tune the dialog summarization policy with Reinforcement Learning. Experiments conducted on multiple datasets demonstrate the effectiveness and generalization of our methods over the state-of-the-art supervised baselines, especially in terms of human judgments.

寻求健康信息的寻求使网络与消费者的健康相关问题淹没了。通常，消费者使用过度描述性和外围信息来表达其医疗状况或其他医疗保健需求，从而有助于自然语言理解的挑战。解决这一挑战的一种方法是总结问题并提取原始问题的关键信息。为了解决此问题，我们介绍了一个新的数据集CHQ-SUMM，其中包含1507个域 - 专家注释的消费者健康问题和相应的摘要。该数据集源自社区提问论坛，因此为了解社交媒体上与消费者健康相关的帖子提供了宝贵的资源。我们在多个最先进的摘要模型上基准测试数据集，以显示数据集的有效性。

维基百科等文本知识库需要相当大的努力来保持最新和一致。虽然自动写作助手可能会缓解这一负担，但探讨了建议在外部知识基础上的建议的问题。在本文中，我们介绍了*忠实地反映了文本*（水果）中的更新信息的新颖一代任务是举办新证据的目标是更新现有文章。我们释放了Fruit-Wiki DataSet，一系列超过170k的远端监督数据，以及我们的数据生成管道和914个实例的金色评估集，其编辑被证据支持。我们为流行的发电系统以及编辑方法提供基准结果 - 根据编辑的基于T5的方法，我们介绍了确定现有技术的。我们的分析表明，开发可以忠实更新文章的开发模型需要新的神经发电模型的新功能，并打开更多新应用程序。