自然语言推理（NLI）任务通常需要通过多个步骤进行推理才能得出结论。尽管产生此类中间步骤的必要性（而不是摘要说明）获得了大众支持，但尚不清楚如何在不完全端到端的监督以及如何进一步利用此类步骤的情况下生成此类步骤。在这项工作中，我们训练一个序列到序列模型，仅生成下一步给定NLI前提和假设对（以及先前的步骤）；然后通过外部知识和符号搜索来增强它，以仅在下一步监督下生成中间步骤。我们通过自动化和人类验证显示了此类生成的步骤的正确性。此外，我们表明，这种生成的步骤可以通过多个公共NLI数据集使用简单的数据增强策略来帮助提高端到端的NLI任务性能。

大型语言模型在各种问题答案（QA）基准测试方面取得了高度的性能，但其产出的解释性仍然难以捉摸。最近建议将结构化的解释称为“综合树”，以解释和检查质量检查系统的答案。为了更好地生成此类树木，我们提出了一种称为迭代检索生成推理​​器（IRGR）的架构。我们的模型能够通过系统地生成文本前提的分步解释来解释给定的假设。 IRGR模型迭代地搜索合适的场所，一次构建单个零件步骤。与以前的方法相反，我们的方法结合了生成步骤和房屋的检索，允许模型利用中间结论，并减轻基线编码器模型的输入大小限制。我们使用IntailmentBank数据集进行实验，在该数据集中，我们在前提检索和索引树上的现有基准优于现有的基准，总体正确性增长了约300％。

Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly improve model interpretability and verification, but objectively studying their correctness (independent of the final answer) is difficult without reliable methods for automatic evaluation. We simply do not know how often the stated reasoning steps actually support the final end task predictions. In this work, we present ROSCOE, a suite of interpretable, unsupervised automatic scores that improve and extend previous text generation evaluation metrics. To evaluate ROSCOE against baseline metrics, we design a typology of reasoning errors and collect synthetic and human evaluation scores on commonly used reasoning datasets. In contrast with existing metrics, ROSCOE can measure semantic consistency, logicality, informativeness, fluency, and factuality - among other traits - by leveraging properties of step-by-step rationales. We empirically verify the strength of our metrics on five human annotated and six programmatically perturbed diagnostics datasets - covering a diverse set of tasks that require reasoning skills and show that ROSCOE can consistently outperform baseline metrics.

我们提出了一种系统推理的方法，该方法生产了基于事实基础的人类可解释的证明树。我们的解决方案类似于经典的基于序言的推理引擎的风格，在该引擎中，我们通过神经语言建模，指导生成和半磁头密集检索的结合来代替手工制作的规则。这款新颖的推理引擎Nellie动态实例化了可解释的推理规则，这些规则捕获和分数构成（DE）在自然语言陈述上。内莉（Nellie）在科学质量检查数据集上提供竞争性能，需要对多个事实进行结构化解释。

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.

可解释的NLP（EXNLP）越来越关注收集人类注释的文本解释。这些解释在三种方面使用下游：作为数据增强，以提高预测任务的性能，因为对培训模型的监督，为他们的预测产生解释，以及评估模型生成的解释的理论。在本次审查中，我们识别65个具有三个主要类别的文本解释的数据集（突出显示，自由文本和结构），组织关于注释每种类型的文献，识别现有收集方法的优势和缺点，并为收集EXNLP数据集提供建议在将来。

As large language models (LLMs) grow larger and more sophisticated, assessing their "reasoning" capabilities in natural language grows more challenging. Recent question answering (QA) benchmarks that attempt to assess reasoning are often limited by a narrow scope of covered situations and subject matters. We introduce WikiWhy, a QA dataset built around a novel auxiliary task: explaining why an answer is true in natural language. WikiWhy contains over 9,000 "why" question-answer-rationale triples, grounded on Wikipedia facts across a diverse set of topics. Each rationale is a set of supporting statements connecting the question to the answer. WikiWhy serves as a benchmark for the reasoning capabilities of LLMs because it demands rigorous explicit rationales for each answer to demonstrate the acquisition of implicit commonsense knowledge, which is unlikely to be easily memorized. GPT-3 baselines achieve only 38.7% human-evaluated correctness in the end-to-end answer & explain condition, leaving significant room for future improvements.

预训练的语言模型（PTLM）已显示出在自然语言任务上表现良好。许多先前的作品都以通过知识图（KGS）标记的关系链接的实体的形式利用结构性常识来协助PTLM。检索方法使用kg作为单独的静态模块，该模块限制了覆盖范围，因为kgs包含有限的知识。生成方法训练PTLMS kg三倍以提高获得知识的规模。但是，对符号KG实体的培训限制了其在涉及自然语言文本的任务中的适用性，在这些任务中，它们忽略了整体上下文。为了减轻这种情况，我们提出了一个以句子为条件的常识性上下文化器（COSE-CO）作为输入，以使其在生成与输入文本的整体上下文相关的任务中通常可用。为了训练Cose-Co，我们提出了一个新的数据集，其中包括句子和常识知识对。 COSE-CO推断出的知识是多种多样的，并且包含了基础KG中不存在的新实体。我们增强了在多选质量质量检查和开放式常识性推理任务中产生的知识，从而改善了CSQA，ARC，QASC和OBQA数据集的当前最佳方法。我们还展示了其在改善释义生成任务的基线模型方面的适用性。

State-of-the-art deep-learning-based approaches to Natural Language Processing (NLP) are credited with various capabilities that involve reasoning with natural language texts. In this paper we carry out a large-scale empirical study investigating the detection of formally valid inferences in controlled fragments of natural language for which the satisfiability problem becomes increasingly complex. We find that, while transformer-based language models perform surprisingly well in these scenarios, a deeper analysis re-veals that they appear to overfit to superficial patterns in the data rather than acquiring the logical principles governing the reasoning in these fragments.

Pre-trained language models (LMs) have shown remarkable reasoning performance using explanations (or ``chain-of-thought'' (CoT)) for in-context learning. On the other hand, these reasoning tasks are usually presumed to be more approachable for symbolic programming. To make progress towards understanding in-context learning, we curate synthetic datasets containing equivalent (natural, symbolic) data pairs, where symbolic examples contain first-order logic rules and predicates from knowledge bases (KBs). Then we revisit neuro-symbolic approaches and use Language Models as Logic Programmer (LMLP) that learns from demonstrations containing logic rules and corresponding examples to iteratively reason over KBs, recovering Prolog's backward chaining algorithm. Comprehensive experiments are included to systematically compare LMLP with CoT in deductive reasoning settings, showing that LMLP enjoys more than 25% higher accuracy than CoT on length generalization benchmarks even with fewer parameters.

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。

We present LogiGAN, an unsupervised adversarial pre-training framework for improving logical reasoning abilities of language models. Upon automatic identifying logical reasoning phenomena in massive text corpus via detection heuristics, we train language models to predict the masked-out logical statements. Inspired by the facilitation effect of reflective thinking in human learning, we analogically simulate the learning-thinking process with an adversarial Generator-Verifier architecture to assist logic learning. LogiGAN implements a novel sequential GAN approach that (a) circumvents the non-differentiable challenge of the sequential GAN by leveraging the Generator as a sentence-level generative likelihood scorer with a learning objective of reaching scoring consensus with the Verifier; (b) is computationally feasible for large-scale pre-training with arbitrary target length. Both base and large size language models pre-trained with LogiGAN demonstrate obvious performance improvement on 12 datasets requiring general reasoning abilities, revealing the fundamental role of logic in broad reasoning, as well as the effectiveness of LogiGAN. Ablation studies on LogiGAN components reveal the relative orthogonality between linguistic and logic abilities and suggest that reflective thinking's facilitation effect might also generalize to machine learning.

Despite the success of large language models (LLMs) in various natural language processing (NLP) tasks, the stored knowledge in these models may inevitably be incomplete, out-of-date, or incorrect. This motivates the need to utilize external knowledge to assist LLMs. Unfortunately, current methods for incorporating external knowledge often require additional training or fine-tuning, which can be costly and may not be feasible for LLMs. To address this issue, we propose a novel post-processing approach, rethinking with retrieval (RR), which retrieves relevant external knowledge based on the decomposed reasoning steps obtained from the chain-of-thought (CoT) prompting. This lightweight approach does not require additional training or fine-tuning and is not limited by the input length of LLMs. We evaluate the effectiveness of RR through extensive experiments with GPT-3 on three complex reasoning tasks: commonsense reasoning, temporal reasoning, and tabular reasoning. Our results show that RR can produce more faithful explanations and improve the performance of LLMs.

在维持预审预定序列模型的灵活性的同时，是否有利于常识性推理，这仍然是一个悬而未决的问题。为了调查这个问题，我们开发了生成的知识提示，该提示包括从语言模型中生成知识，然后在回答问题时提供知识作为附加输入。我们的方法不需要特定于任务的监督知识集成或访问结构化的知识库，但它可以提高四个常识性推理任务上的大规模，最先进的模型的性能，从而实现最先进-ART结果取决于数值常识（NumerSense），通用常识性（Commonsenseqa 2.0）和科学常识（QASC）基准。产生的知识促使大型语言模型是灵活的外部知识来源，以改善常识性推理。我们的代码可从https://github.com/liujch1998/gkp获得

当前的抽象摘要模型要么仅通过突出源文档的一部分而缺乏明显的解释性或提供不完整的理由。为此，我们提出了摘要程序（SP），这是一个由二进制树的（有序）列表组成的可解释的模块化框架，每个框架都编码来自源文档的抽象摘要句子的分步生成过程。一个摘要程序每个摘要句子包含一个根节点，一棵不同的树将每个摘要句子（根节点）连接到派生的文档句子（叶节点），其中包含中间生成的句子的连接节点。边缘代表涉及摘要的不同模块化操作，例如句子融合，压缩和释义。我们首先建议通过神经模块提出有效的最佳搜索方法，SP搜索通过直接优化Rouge分数来识别人类摘要的SP搜索。接下来，使用这些程序作为自动监督，我们建议使用生成摘要程序的SEQ2SEQ模型，然后执行以获取最终摘要。我们证明，SP搜索有效地代表了使用通常忠于其预期行为的模块的人类摘要背后的生成过程。我们还进行了一项仿真研究，以表明汇总计划通过允许人类更好地模拟模型推理来改善摘要模型的解释性。汇总计划构成了朝着可解释和模块化的抽象摘要迈出的有希望的步骤，这是先前主要通过黑框端到端神经系统解决的复杂任务。我们的代码可从https://github.com/swarnahub/summarization Programs获得

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。

人类使用自然语言来撰写普通概念，将他们的环境归结为合理的日常场景描述。然而，这种生成的致辞推理（GCSR）技能缺乏最先进的文本生成方法。关于由神经文本生成模型（例如，预先接受的文本到文本变压器）生成的任意概念的描述性句子通常是语法流畅的，但可能与人类常识不相符，这主要是由于它们缺乏捕获概念关系的机制识别隐式概念，并对看不见的概念组成来执行概括的推理。在本文中，我们提出了一种想象的 - 言语（I＆V）方法，其学会在输入概念之间的关系中想象一个关系场景知识图（SKG），并在生成合理的场景描述时利用SKG作为约束。我们收集和协调来自不同领域和方式的一套知识资源，为I＆v提供丰富的辅助监督信号。该实验展示了I＆V在提高概念到句子和概念到故事的生成任务上的语言模型的有效性，同时使模型能够从更少的任务示例中学习并生成对人类注入者常识的SKG。

从头开始解决复杂问题通常是有挑战性的，但如果我们可以访问其解决方案的其他类似问题，则更容易 - 一种称为基于案例的推理（CBR）的范式。我们提出了一种神经象征性的CBR方法（CBR-KBQA），用于在大知识库上应答。 CBR-KBQA由非参数内存组成，该内存存储案例（问题和逻辑表单）和参数模型，该参数模型可以通过检索与其相关的案例来为新问题生成逻辑表单。在包含复杂问题的几个KBQA数据集上，CBR-KBQA实现了竞争性能。例如，在ComplexWebQuestions数据集上，CBR-KBQA以11 \％的准确度优于当前最新状态。此外，我们表明CBR-KBQA能够使用新案例\ EMPH {没有}任何进一步的培训：通过在案例存储器中纳入一些人类标记的示例，CBR-KBQA能够成功地生成包含未经看线KB实体的逻辑表格以及关系。

This paper investigates models of event implications. Specifically, how well models predict entity state-changes, by targeting their understanding of physical attributes. Nominally, Large Language models (LLM) have been exposed to procedural knowledge about how objects interact, yet our benchmarking shows they fail to reason about the world. Conversely, we also demonstrate that existing approaches often misrepresent the surprising abilities of LLMs via improper task encodings and that proper model prompting can dramatically improve performance of reported baseline results across multiple tasks. In particular, our results indicate that our prompting technique is especially useful for unseen attributes (out-of-domain) or when only limited data is available.

传统的象征性推理发动机，同时有吸引力的精度和可剥削性，具有一些主要缺点：使用依赖于逻辑术语的完全匹配（统一）的脆性推理程序的使用，无法应对不确定性，并需要对预调锋相同的需求规则基础（“知识获取”问题）。为了解决这些问题，我们设计了一个名为辫子的新颖逻辑推理，支持概率规则，并利用自定义统一功能和动态规则生成的概念来克服传统资料中普遍存在的脆性匹配和知识差距问题。在本文中，我们描述了编织中使用的推理算法，以及它们在基于分布式任务的框架中的实现，为输入查询构建证明/解释图。我们使用一个简单的QA示例来自儿童故事来激励辫子的设计，并解释各种组件如何共同努力，以产生一致的逻辑解释。最后，我们评估Roc Story Cloze测试的编织，并在提供基于帧的解释的同时实现近最先进的结果。