Recent methods demonstrate that data augmentation using counterfactual knowledge can teach models the causal structure of a task, leading to robust and generalizable models. However, such counterfactual data often has a limited scale and diversity if crowdsourced and is computationally expensive to extend to new perturbation types if generated using supervised methods. To address this, we introduce a new framework called DISCO for automatically generating high-quality counterfactual data at scale. DISCO engineers prompts to generate phrasal perturbations with a large general language model. Then, a task-specific teacher model filters the generation to distill high-quality counterfactual data. We show that learning with this counterfactual data yields a comparatively small student model that is 6% (absolute) more robust and generalizes 5% better across distributions than baselines on various challenging evaluations. This model is also 15% more sensitive in differentiating original and counterfactual examples, on three evaluation sets written by human workers and via human-AI collaboration.

Recent studies have shown the impressive efficacy of counterfactually augmented data (CAD) for reducing NLU models' reliance on spurious features and improving their generalizability. However, current methods still heavily rely on human efforts or task-specific designs to generate counterfactuals, thereby impeding CAD's applicability to a broad range of NLU tasks. In this paper, we present AutoCAD, a fully automatic and task-agnostic CAD generation framework. AutoCAD first leverages a classifier to unsupervisedly identify rationales as spans to be intervened, which disentangles spurious and causal features. Then, AutoCAD performs controllable generation enhanced by unlikelihood training to produce diverse counterfactuals. Extensive evaluations on multiple out-of-domain and challenge benchmarks demonstrate that AutoCAD consistently and significantly boosts the out-of-distribution performance of powerful pre-trained models across different NLU tasks, which is comparable or even better than previous state-of-the-art human-in-the-loop or task-specific CAD methods. The code is publicly available at https://github.com/thu-coai/AutoCAD.

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。

由于NLP模型实现了基准测试的最先进的性能并获得了广泛的应用程序，因此确保在现实世界中的安全部署这些模型的安全部署，例如，确保模型对未经调用或具有挑战性的情景稳健。尽管具有越来越多的学习主题，但它在视觉和NLP等应用中分别探讨了，具有多种研究中的各种定义，评估和缓解策略。在本文中，我们的目标是提供对如何定义，测量和提高NLP鲁棒性的统一调查。我们首先连接多种稳健性的定义，然后统一各种各样的工作方面识别稳健性失败和评估模型的鲁棒性。相应地，我们呈现了数据驱动，模型驱动和基于归纳的缓解策略，具有如何有效地改善NLP模型中的鲁棒性的更系统的观点。最后，我们通过概述开放的挑战和未来方向来促进在这一领域的进一步研究。

The common practice for training commonsense models has gone from-human-to-corpus-to-machine: humans author commonsense knowledge graphs in order to train commonsense models. In this work, we investigate an alternative, from-machine-to-corpus-to-machine: general language models author these commonsense knowledge graphs to train commonsense models. Our study leads to a new framework, Symbolic Knowledge Distillation. As with prior art in Knowledge Distillation (Hinton et al., 2015), our approach uses larger models to teach smaller models. A key difference is that we distill knowledge symbolically-as text-in addition to the neural model. We also distill only one aspect-the commonsense of a general language model teacher, allowing the student to be a different type, a commonsense model. Altogether, we show that careful prompt engineering and a separately trained critic model allow us to selectively distill high-quality causal commonsense from GPT-3, a general language model. Empirical results demonstrate that, for the first time, a human-authored commonsense knowledge graph is surpassed by our automatically distilled variant in all three criteria: quantity, quality, and diversity. In addition, it results in a neural commonsense model that surpasses the teacher model's commonsense capabilities despite its 100x smaller size. We apply this to the ATOMIC resource, and share our new symbolic knowledge graph and commonsense models.

Temporal reasoning is the task of predicting temporal relations of event pairs with corresponding contexts. While some temporal reasoning models perform reasonably well on in-domain benchmarks, we have little idea of the systems' generalizability due to existing datasets' limitations. In this work, we introduce a novel task named TODAY that bridges this gap with temporal differential analysis, which as the name suggests, evaluates if systems can correctly understand the effect of incremental changes. Specifically, TODAY makes slight context changes for given event pairs, and systems need to tell how this subtle contextual change will affect temporal relation distributions. To facilitate learning, TODAY also annotates human explanations. We show that existing models, including GPT-3, drop to random guessing on TODAY, suggesting that they heavily rely on spurious information rather than proper reasoning for temporal predictions. On the other hand, we show that TODAY's supervision style and explanation annotations can be used in joint learning and encourage models to use more appropriate signals during training and outperform across several benchmarks. TODAY can also be used to train models to solicit incidental supervision from noisy sources such as GPT-3 and moves farther towards generic temporal reasoning systems.

几乎没有命名的实体识别（NER）对于在有限的资源领域中标记的实体标记至关重要，因此近年来受到了适当的关注。现有的几声方法主要在域内设置下进行评估。相比之下，对于这些固有的忠实模型如何使用一些标记的域内示例在跨域NER中执行的方式知之甚少。本文提出了一种两步以理性为中心的数据增强方法，以提高模型的泛化能力。几个数据集中的结果表明，与先前的最新方法相比，我们的模型无形方法可显着提高跨域NER任务的性能，包括反事实数据增强和及时调用方法。我们的代码可在\ url {https://github.com/lifan-yuan/factmix}上获得。

With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few training examples. It has been a new trend exploring ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress, challenges, and future work in ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques of ICL, including training strategies, prompting strategies, and so on. Finally, we present the challenges of ICL and provide potential directions for further research. We hope our work can encourage more research on uncovering how ICL works and improving ICL in future work.

Data Augmentation (DA) is frequently used to automatically provide additional training data without extra human annotation. However, data augmentation may introduce noisy data that impairs training. To guarantee the quality of augmented data, existing methods either assume no noise exists in the augmented data and adopt consistency training or use simple heuristics such as training loss and diversity constraints to filter out ``noisy'' data. However, those filtered examples may still contain useful information, and dropping them completely causes loss of supervision signals. In this paper, based on the assumption that the original dataset is cleaner than the augmented data, we propose an on-the-fly denoising technique for data augmentation that learns from soft augmented labels provided by an organic teacher model trained on the cleaner original data. A simple self-regularization module is applied to force the model prediction to be consistent across two distinct dropouts to further prevent overfitting on noisy labels. Our method can be applied to augmentation techniques in general and can consistently improve the performance on both text classification and question-answering tasks.

数据增强是通过转换为机器学习的人工创建数据的人工创建，是一个跨机器学习学科的研究领域。尽管它对于增加模型的概括功能很有用，但它还可以解决许多其他挑战和问题，从克服有限的培训数据到正规化目标到限制用于保护隐私的数据的数量。基于对数据扩展的目标和应用的精确描述以及现有作品的分类法，该调查涉及用于文本分类的数据增强方法，并旨在为研究人员和从业者提供简洁而全面的概述。我们将100多种方法划分为12种不同的分组，并提供最先进的参考文献来阐述哪种方法可以通过将它们相互关联，从而阐述了哪种方法。最后，提供可能构成未来工作的基础的研究观点。

Neural language models (LMs) have achieved impressive results on various language-based reasoning tasks by utilizing latent knowledge encoded in their own pretrained parameters. To make this reasoning process more explicit, recent works retrieve a rationalizing LM's internal knowledge by training or prompting it to generate free-text rationales, which can be used to guide task predictions made by either the same LM or a separate reasoning LM. However, rationalizing LMs require expensive rationale annotation and/or computation, without any assurance that their generated rationales improve LM task performance or faithfully reflect LM decision-making. In this paper, we propose PINTO, an LM pipeline that rationalizes via prompt-based learning, and learns to faithfully reason over rationales via counterfactual regularization. First, PINTO maps out a suitable reasoning process for the task input by prompting a frozen rationalizing LM to generate a free-text rationale. Second, PINTO's reasoning LM is fine-tuned to solve the task using the generated rationale as context, while regularized to output less confident predictions when the rationale is perturbed. Across four datasets, we show that PINTO significantly improves the generalization ability of the reasoning LM, yielding higher performance on both in-distribution and out-of-distribution test sets. Also, we find that PINTO's rationales are more faithful to its task predictions than those generated by competitive baselines.

In many task settings, text classification models are likely to encounter examples from novel classes on which they cannot predict correctly. Selective prediction, in which models abstain on low-confidence examples, provides a possible solution, but existing models are often overly confident on OOD examples. To remedy this overconfidence, we introduce Contrastive Novelty-Augmented Learning (CoNAL), a two-step method that generates OOD examples representative of novel classes, then trains to decrease confidence on them. First, we generate OOD examples by prompting a large language model twice: we prompt it to enumerate relevant novel labels, then generate examples from each novel class matching the task format. Second, we train our classifier with a novel contrastive objective that encourages lower confidence on generated OOD examples than training examples. When trained with CoNAL, classifiers improve in their ability to detect and abstain on OOD examples over prior methods by an average of 2.3% AUAC and 5.5% AUROC across 4 NLP datasets, with no cost to in-distribution accuracy.

在基于文本的分类器中测试公平性问题的一种常见方法是通过使用反事实来：如果更改输入中的敏感属性，则分类器输出是否会更改？现有的反事实生成方法通常依赖于单词列表或模板，产生不考虑语法，上下文或微妙敏感属性引用的简单反事实，并且可能会错过WordList创建者未考虑的问题。在本文中，我们介绍了一项为克服这些缺点而产生的反事实的任务，并证明了如何利用大型语言模型（LLM）来在此任务上取得进展。我们表明，这种基于LLM的方法可以产生现有方法无法实现的复杂反事实，从而比较了民事评论数据集中各种反事实生成方法的性能，并在评估毒性分类器时显示出它们的价值。

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.

创建什么故事需要推理关于先前陈述以及变更条件的可能结果。人们可以在新条件下轻松生成连贯的结局，但目前系统会对原始故事进行最小的变化来挑战。因此，一个主要挑战是生成逻辑故事和用最小编辑重写之间的权衡。在本文中，我们提出了教育，这是一种基于编辑的无预测方法，用于反复重写。教育包括基于估计在线条件的因果效果的目标位置检测策略，这使故事的因果不变部分。 Bowat然后在流畅，一致性和最小编辑约束下生成故事。我们还提出了一种新的指标来缓解当前自动指标的缺点，更好地评估权衡。我们评估公共反事故事重写基准的教育。实验表明，教育根据自动和人类评估，达到了无监督的SOTA方法的最佳权衡。教育资源可用于：https：//github.com/jiangjiechen/educat。

Language models (LMs) have demonstrated remarkable performance on downstream tasks, using in-context exemplars or human instructions. Recent works have shown that chain-of-thought (CoT) prompting can elicit models to solve complex reasoning tasks, step-by-step. However, the efficacy of prompt-based CoT methods is restricted to very large LMs such as GPT-3 (175B), thus limiting deployability. In this paper, we revisit the fine-tuning approach to enable complex reasoning in smaller LMs, optimized to efficiently perform a specific task. We propose Fine-tune-CoT, a method that leverages the capabilities of very large LMs to generate reasoning samples and teach smaller models via fine-tuning. We evaluate our method on publicly available LMs across a wide range of complex tasks and model sizes. We find that Fine-tune-CoT enables substantial reasoning capability in small models, whereas previous prompt-based baselines exhibit near-random performance. Student models can even outperform the teacher in some tasks while reducing model size requirements by several orders of magnitude. We conduct extensive ablations and sample studies to understand the reasoning capabilities of student models. We also identify several important nuances that have been overlooked in concurrent fine-tuning works on CoT and address them in our analysis.

大型语言模型（LLM）已在一系列自然语言理解任务上实现了最先进的表现。但是，这些LLM可能依靠数据集偏差和文物作为预测的快捷方式。这极大地损害了他们的分布（OOD）概括和对抗性鲁棒性。在本文中，我们对最新发展的综述，这些发展解决了LLMS的鲁棒性挑战。我们首先介绍LLM的概念和鲁棒性挑战。然后，我们介绍了在LLM中识别快捷方式学习行为的方法，表征了快捷方式学习的原因以及引入缓解解决方案。最后，我们确定了关键挑战，并将这一研究线的联系引入其他方向。

为了创建在广泛的测试输入中强大的模型，培训数据集应包括跨越许多现象的各种示例。动态的对抗数据收集（DADC），注释者制作的示例挑战不断改进模型的示例，其有望是生成这种多样化训练集的一种方法。先前的工作表明，在1-3轮中运行DADC可以帮助模型修复某些错误类型，但不一定会带来更好的概括，而不是对抗性测试数据。我们认为，在许多回合中运行DADC可以最大化其训练时间的好处，因为不同的回合可以涵盖许多与任务相关的现象。我们介绍了长期DADC的第一个研究，其中我们收集了20轮NLI示例，用于一小部分前提，并采用对抗性和非对抗性方法。与接受非对抗数据的训练的模型相比，接受DADC示例培训的模型在我们的专家策划测试集中的错误少26％。我们的分析表明，DADC产生的例子更加困难，更词法和句法多样性，并且与非对抗性示例相比，注释伪像更少。

大型语言模型越来越能够通过相对较少的特定任务的监督产生流畅的出现文本。但这些模型可以准确解释分类决策吗？我们考虑使用少量人写的例子（即，以几滴方式）生成自由文本解释的任务。我们发现（1）创作更高质量的例子，以提示导致更高质量的世代; （2）令人惊讶的是，在头到头比较中，人群公司通常更喜欢GPT-3生成的解释，以众包中包含的人性写入的解释。然而，Crowdworker评级也表明，虽然模型产生了事实，语法和充分的解释，但它们具有改进的空间，例如沿着提供新颖信息和支持标签的轴。我们创建了一种管道，该管道将GPT-3与监督过滤器结合起来，该过滤器通过二进制可接受性判断来包含人类循环。尽管具有重要的主观性内在的判断可接受性，但我们的方法能够始终如一地过滤人类可接受的GPT-3生成的解释。

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.