大规模的预训练的语言模型在自然语言生成任务上取得了巨大的成功。但是，很难控制预先训练的语言模型来生成具有所需属性的句子，例如主题和情感等。最近，贝叶斯可控的语言模型（BCLM）已被证明在可控制的语言生成中有效。 BCLM并没有微调预训练的语言模型的参数，而是使用外部歧视器来指导预训练的语言模型的生成。但是，BCLMS训练和推断之间的不匹配限制了模型的性能。为了解决这个问题，在这项工作中，我们为可控语言生成提出了一个“双子座歧视者”，以减轻小计算成本的不匹配问题。我们在两个可控的语言生成任务上测试了我们的方法：情感控制和主题控制。在这两项任务上，我们的方法都达到了新的最先进的结果，从而可以自动评估。

控制语言模型的主要方法在控制高级属性（例如主题和情感）方面具有突出性。但是，这些方法通常需要特定于条件的数据或计算昂贵。我们提出了一种新的简单引导解码方法，伽玛采样，该方法不需要任何培训数据来实现可控制的文本生成，同时保持快速生成速度。伽玛采样将与属性相关的信息（由人类或语言模型本身提供）引入采样过程中，以指导语言模型，以生成具有所需属性的文本。由于不涉及培训，因此可以轻松地将伽马抽样应用于任何语言模型以进行可控文本。通过实验，我们表明，伽马取样的GPT2-MALL（1.17亿）优于PPLM（345m）和CTRL（1.6B）的多样性，属性相关性以及生成样品的整体质量。

Controllable Text Generation (CTG) is emerging area in the field of natural language generation (NLG). It is regarded as crucial for the development of advanced text generation technologies that are more natural and better meet the specific constraints in practical applications. In recent years, methods using large-scale pre-trained language models (PLMs), in particular the widely used transformer-based PLMs, have become a new paradigm of NLG, allowing generation of more diverse and fluent text. However, due to the lower level of interpretability of deep neural networks, the controllability of these methods need to be guaranteed. To this end, controllable text generation using transformer-based PLMs has become a rapidly growing yet challenging new research hotspot. A diverse range of approaches have emerged in the recent 3-4 years, targeting different CTG tasks which may require different types of controlled constraints. In this paper, we present a systematic critical review on the common tasks, main approaches and evaluation methods in this area. Finally, we discuss the challenges that the field is facing, and put forward various promising future directions. To the best of our knowledge, this is the first survey paper to summarize CTG techniques from the perspective of PLMs. We hope it can help researchers in related fields to quickly track the academic frontier, providing them with a landscape of the area and a roadmap for future research.

预处理的基于变压器的语言模型（LMS）显示出显着的自然语言生成能力。凭借其巨大的潜力，控制这种LM的文本生成引起了人们的关注。尽管有一些研究试图控制生成的文本的高级属性（例如情感和主题），但仍然缺乏对其在单词和短语级别上的内容的更精确的控制。在这里，我们建议内容调节器（COCON）以细粒度的水平控制LM的输出文本。在我们的自我监督方法中，Cocon Block学会了通过调节从LM中扣留的内容输入来帮助LM完成部分观察到的文本序列。通过实验，我们表明Cocon可以自然地将目标内容纳入生成的文本中，并以零拍的方式控制高级文本属性。

Steering language generation towards objectives or away from undesired content has been a long-standing goal in utilizing language models (LM). Recent work has demonstrated reinforcement learning and weighted decoding as effective approaches to achieve a higher level of language control and quality with pros and cons. In this work, we propose a novel critic decoding method for controlled language generation (CriticControl) that combines the strengths of reinforcement learning and weighted decoding. Specifically, we adopt the actor-critic framework to train an LM-steering critic from non-differentiable reward models. And similar to weighted decoding, our method freezes the language model and manipulates the output token distribution using called critic, improving training efficiency and stability. Evaluation of our method on three controlled generation tasks, namely topic control, sentiment control, and detoxification, shows that our approach generates more coherent and well-controlled texts than previous methods. In addition, CriticControl demonstrates superior generalization ability in zero-shot settings. Human evaluation studies also corroborate our findings.

Large pretrained language models generate fluent text but are notoriously hard to controllably sample from. In this work, we study constrained sampling from such language models: generating text that satisfies user-defined constraints, while maintaining fluency and the model's performance in a downstream task. We propose MuCoLa -- a sampling procedure that combines the log-likelihood of the language model with arbitrary (differentiable) constraints in a single energy function, and then generates samples in a non-autoregressive manner. Specifically, it initializes the entire output sequence with noise and follows a Markov chain defined by Langevin Dynamics using the gradients of the energy function. We evaluate MuCoLa on text generation with soft and hard constraints as well as their combinations obtaining significant improvements over competitive baselines for toxicity avoidance, sentiment control, and keyword-guided generation.

预先接受的语言模型实现了最先进的导致各种自然语言处理（NLP）任务。 GPT-3表明，缩放预先训练的语言模型可以进一步利用它们的巨大潜力。最近提出了一个名为Ernie 3.0的统一框架，以预先培训大型知识增强型号，并培训了具有10亿参数的模型。 Ernie 3.0在各种NLP任务上表现出最先进的模型。为了探讨缩放的表现，我们培养了百卢比的3.0泰坦参数型号，在PaddlePaddle平台上有高达260亿参数的泰坦。此外，我们设计了一种自我监督的对抗性损失和可控语言建模损失，以使ERNIE 3.0 TITAN产生可信和可控的文本。为了减少计算开销和碳排放，我们向Ernie 3.0泰坦提出了一个在线蒸馏框架，教师模型将同时教授学生和培训。埃塞尼3.0泰坦是迄今为止最大的中国密集预训练模型。经验结果表明，Ernie 3.0泰坦在68个NLP数据集中优于最先进的模型。

真实世界的文本应用程序通常涉及组成广泛的文本控制操作，例如编辑文本W.R.T.属性，操纵关键字和结构，并生成所需属性的新文本。事先的工作通常会学习/芬太尼语言模型（LM）以执行操作的个人或特定子集。最近的研究以插件方式研究了合并操作，通常在复杂序列空间中以昂贵的搜索或优化进行了研究。本文提出了一种新的有效方法，用于在紧凑的文本潜在空间中进行可复合的文本操作。文本潜在矢量的低维度和不同性使我们能够基于给定的任意插入运算符（例如属性分类器）基于普通微分方程（ODE）开发有效的采样器。通过通过有效的适应性将预告片的LMS（例如GPT2）连接到潜在空间，然后我们将采样向量解码为所需的文本序列。灵活的方法允许使用来自不同域中的任何相关数据获取的各种控制操作员（情感，时态，形式，关键字等）。实验表明，在我们的方法中构成这些操作员可以生成或编辑高质量文本，从而在发电质量和效率方面显着改善了以前的方法。

Existing reference-free metrics have obvious limitations for evaluating controlled text generation models. Unsupervised metrics can only provide a task-agnostic evaluation result which correlates weakly with human judgments, whereas supervised ones may overfit task-specific data with poor generalization ability to other datasets. In this paper, we propose an unsupervised reference-free metric called CTRLEval, which evaluates controlled text generation from different aspects by formulating each aspect into multiple text infilling tasks. On top of these tasks, the metric assembles the generation probabilities from a pre-trained language model without any model training. Experimental results show that our metric has higher correlations with human judgments than other baselines, while obtaining better generalization of evaluating generated texts from different models and with different qualities.

深度神经语言模型的最新进展与大规模数据集的能力相结合，加速了自然语言生成系统的发展，这些系统在多种任务和应用程序上下文中产生流利和连贯的文本（在各种成功程度上）。但是，为所需的用户控制这些模型的输出仍然是一个开放的挑战。这不仅对于自定义生成语言的内容和样式至关重要，而且对于他们在现实世界中的安全可靠部署至关重要。我们提出了一项关于受约束神经语言生成的新兴主题的广泛调查，在该主题中，我们通过区分条件和约束（后者是在输出文本上而不是输入的可检验条件），正式定义和分类自然语言生成问题，目前是可检验的）约束文本生成任务，并查看受限文本生成的现有方法和评估指标。我们的目的是强调这个新兴领域的最新进展和趋势，以告知最有希望的方向和局限性，以推动受约束神经语言生成研究的最新作品。

Pretrained language models have demonstrated extraordinary capabilities in language generation. However, real-world tasks often require controlling the distribution of generated text in order to mitigate bias, promote fairness, and achieve personalization. Existing techniques for controlling the distribution of generated text only work with quantified distributions, which require pre-defined categories, proportions of the distribution, or an existing corpus following the desired distributions. However, many important distributions, such as personal preferences, are unquantified. In this work, we tackle the problem of generating text following arbitrary distributions (quantified and unquantified) by proposing Nano, a few-shot human-in-the-loop training algorithm that continuously learns from human feedback. Nano achieves state-of-the-art results on single topic/attribute as well as quantified distribution control compared to previous works. We also show that Nano is able to learn unquantified distributions, achieves personalization, and captures differences between different individuals' personal preferences with high sample efficiency.

Self-training (ST) has prospered again in language understanding by augmenting the fine-tuning of pre-trained language models when labeled data is insufficient. However, it remains challenging to incorporate ST into attribute-controllable language generation. Augmented by only self-generated pseudo text, generation models over-emphasize exploitation of the previously learned space, suffering from a constrained generalization boundary. We revisit ST and propose a novel method, DuNST to alleviate this problem. DuNST jointly models text generation and classification with a shared Variational AutoEncoder and corrupts the generated pseudo text by two kinds of flexible noise to disturb the space. In this way, our model could construct and utilize both pseudo text from given labels and pseudo labels from available unlabeled text, which are gradually refined during the ST process. We theoretically demonstrate that DuNST can be regarded as enhancing exploration towards the potential real text space, providing a guarantee of improved performance. Experiments on three controllable generation tasks show that DuNST could significantly boost control accuracy while maintaining comparable generation fluency and diversity against several strong baselines.

近年来，文本的风格特性吸引了计算语言学研究人员。具体来说，研究人员研究了文本样式转移（TST）任务，该任务旨在在保留其样式独立内容的同时改变文本的风格属性。在过去的几年中，已经开发了许多新颖的TST算法，而该行业利用这些算法来实现令人兴奋的TST应用程序。由于这种共生，TST研究领域迅速发展。本文旨在对有关文本样式转移的最新研究工作进行全面审查。更具体地说，我们创建了一种分类法来组织TST模型，并提供有关最新技术状况的全面摘要。我们回顾了针对TST任务的现有评估方法，并进行了大规模的可重复性研究，我们在两个公开可用的数据集上实验基准了19个最先进的TST TST算法。最后，我们扩展了当前趋势，并就TST领域的新开发发展提供了新的观点。

长篇小说（LSG）是自然语言处理中的垂灾目标之一。与大多数文本生成任务不同，LSG要求基于更短的文本输入输出丰富的内容的长话，并且通常存在信息稀疏性。在本文中，我们提出了\ emph {topnet}来缓解这个问题，通过利用神经主题建模的最新进步来获得高质量的骨架词来补充短输入。特别是，而不是直接生成故事，首先学会将简短的文本输入映射到低维主题分布（由主题模型预先分配）。基于此潜在主题分布，我们可以使用主题模型的重建解码器来对与故事的骨骼相互相关的单词序列。两个基准数据集的实验表明，我们的框架在骨架词选择中非常有效，在自动评估和人类评估中显着优于最先进的模型。

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.

在本文中，我们使用大规模播放脚本数据集来提出从对话中提出戏剧发电的新颖任务。使用超过一百万行的对话和提示，我们将提示生成问题作为受控文本生成任务方法，并展示如何使用如何使用对话/提示鉴别器的语言模型来增强对话的影响。此外，我们还探讨了主题关键字和情绪的使用，以获得受控文本生成。广泛的定量和定性实验表明，语言模型可以成功地用于在高度专业化的域中生成合理的和属性控制的文本，例如播放脚本。配套材料可在：https://catlab-team.github.io/cuegen。

GPT-3等大型语言模型是优秀的几次学习者，允许他们通过自然文本提示来控制。最近的研究报告称，基于及时的直接分类消除了对微调的需求，但缺乏数据和推理可扩展性。本文提出了一种新的数据增强技术，利用大规模语言模型来生成来自真实样本的混合的现实文本样本。我们还建议利用语言模型预测的软标签，从大规模语言模型中有效地蒸馏知识并同时创建文本扰动。我们对各种分类任务进行数据增强实验，并显示我们的方法非常优于现有的文本增强方法。消融研究和定性分析为我们的方法提供了更多的见解。

我们提出了两种小型无监督方法，用于消除文本中的毒性。我们的第一个方法结合了最近的两个想法：（1）使用小型条件语言模型的生成过程的指导和（2）使用释义模型进行风格传输。我们使用良好的令人措辞的令人愉快的释放器，由风格培训的语言模型引导，以保持文本内容并消除毒性。我们的第二种方法使用BERT用他们的非攻击性同义词取代毒性单词。我们通过使BERT替换具有可变数量的单词的屏蔽令牌来使该方法更灵活。最后，我们介绍了毒性去除任务的风格转移模型的第一个大规模比较研究。我们将模型与许多用于样式传输的方法进行比较。使用无监督的样式传输指标的组合以可参考方式评估该模型。两种方法都建议产生新的SOTA结果。

大型预训练的语言模型能够产生多种多样的文本。从提示开始，这些模型产生了一种可以不可预测的叙述。现有的可控文本生成方法，该方法指导用户指定方向的文本中的叙述，需要创建培训语料库和额外的耗时培训程序。本文提出并调查了Contocation2Text，这是一种用于俄罗斯自动可控文本生成的插件方法，不需要微调。该方法基于两个交互模型：自回归语言Rugpt-3模型和自动编码语言Ruroberta模型。该方法的想法是根据自动编码模型的输出分布将自回归模型的输出分布移动，以确保文本中叙事的连贯过渡向指南短语，其中可以包含单个单词或搭配。能够考虑到令牌的左和右下方的自动编码模型“告诉”“自动回归模型”在当前一代步骤中，该模型是令牌最不合逻辑的，从而增加或降低了相应令牌的概率。使用该方法生成新闻文章的实验显示了其对自动生成的流利文本的有效性，这些文本包含用户指定的短语之间的连贯过渡。

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.