Current pre-trained language models have enabled remarkable improvements in downstream tasks, but it remains difficult to distinguish effects of statistical correlation from more systematic logical reasoning grounded on understanding of the real world. In this paper we tease these factors apart by leveraging counterfactual conditionals, which force language models to predict unusual consequences based on hypothetical propositions. We introduce a set of tests drawn from psycholinguistic experiments, as well as larger-scale controlled datasets, to probe counterfactual predictions from a variety of popular pre-trained language models. We find that models are consistently able to override real-world knowledge in counterfactual scenarios, and that this effect is more robust in case of stronger baseline world knowledge -- however, we also find that for most models this effect appears largely to be driven by simple lexical cues. When we mitigate effects of both world knowledge and lexical cues to test knowledge of linguistic nuances of counterfactuals, we find that only GPT-3 shows sensitivity to these nuances, though this sensitivity is also non-trivially impacted by lexical associative factors.

我们研究了现代神经语言模型容易受到结构启动的程度，这种现象使句子的结构在后续句子中更有可能使相同的结构更有可能。我们探索如何使用启动来研究这些模型学习抽象结构信息的潜力，这是需要自然语言理解技能的任务良好表现的先决条件。我们引入了一种新型的度量标准和释放Prime-LM，这是一个大型语料库，我们可以控制与启动强度相互作用的各种语言因素。我们发现，变压器模型确实显示了结构启动的证据，但他们所学到的概括在某种程度上是由语义信息调节的。我们的实验还表明，模型获得的表示不仅可以编码抽象的顺序结构，而且还涉及一定级别的层次句法信息。更普遍的是，我们的研究表明，启动范式是一种有用的，可用于洞悉语言模型能力的有用的，并为未来的基于底漆的调查打开了探测模型内部状态的未来大门。

Targeted syntactic evaluations of language models ask whether models show stable preferences for syntactically acceptable content over minimal-pair unacceptable inputs. Most targeted syntactic evaluation datasets ask models to make these judgements with just a single context-free sentence as input. This does not match language models' training regime, in which input sentences are always highly contextualized by the surrounding corpus. This mismatch raises an important question: how robust are models' syntactic judgements in different contexts? In this paper, we investigate the stability of language models' performance on targeted syntactic evaluations as we vary properties of the input context: the length of the context, the types of syntactic phenomena it contains, and whether or not there are violations of grammaticality. We find that model judgements are generally robust when placed in randomly sampled linguistic contexts. However, they are substantially unstable for contexts containing syntactic structures matching those in the critical test content. Among all tested models (GPT-2 and five variants of OPT), we significantly improve models' judgements by providing contexts with matching syntactic structures, and conversely significantly worsen them using unacceptable contexts with matching but violated syntactic structures. This effect is amplified by the length of the context, except for unrelated inputs. We show that these changes in model performance are not explainable by simple features matching the context and the test inputs, such as lexical overlap and dependency overlap. This sensitivity to highly specific syntactic features of the context can only be explained by the models' implicit in-context learning abilities.

基于变压器的语言模型最近在许多自然语言任务中取得了显着的结果。但是，通常通过利用大量培训数据来实现排行榜的性能，并且很少通过将明确的语言知识编码为神经模型。这使许多人质疑语言学对现代自然语言处理的相关性。在本文中，我介绍了几个案例研究，以说明理论语言学和神经语言模型仍然相互关联。首先，语言模型通过提供一个客观的工具来测量语义距离，这对语言学家很有用，语义距离很难使用传统方法。另一方面，语言理论通过提供框架和数据源来探究我们的语言模型，以了解语言理解的特定方面，从而有助于语言建模研究。本论文贡献了三项研究，探讨了语言模型中语法 - 听觉界面的不同方面。在论文的第一部分中，我将语言模型应用于单词类灵活性的问题。我将Mbert作为语义距离测量的来源，我提供了有利于将单词类灵活性分析为方向过程的证据。在论文的第二部分中，我提出了一种方法来测量语言模型中间层的惊奇方法。我的实验表明，包含形态句法异常的句子触发了语言模型早期的惊喜，而不是语义和常识异常。最后，在论文的第三部分中，我适应了一些心理语言学研究，以表明语言模型包含了论证结构结构的知识。总而言之，我的论文在自然语言处理，语言理论和心理语言学之间建立了新的联系，以为语言模型的解释提供新的观点。

虽然句子异常已经定期应用于NLP中的测试，但我们尚未建立从NLP模型中的表示中的异常信息的确切状态的图片。在本文中，我们的目标是填补两个主要间隙，重点关注句法异常的领域。首先，我们通过设计改变异常在句子中发生的分层级别的探测任务来探讨异常编码的细粒度差异。其次，我们不仅测试了模型能够通过检查不同异常类型之间的转移来检测给定异常的能力，还能检测给定的异常信号的一般性。结果表明，所有型号都编码一些支持异常检测的信息，但检测性能在异常之间变化，并且只有最近的变压器模型的唯一表示显示了异常知识的概括知识的迹象。随访分析支持这些模型在合法的句子奇迹上接受合法的概念，而粗糙的单词位置信息也可能是观察到的异常检测的贡献者。

People constantly use language to learn about the world. Computational linguists have capitalized on this fact to build large language models (LLMs) that acquire co-occurrence-based knowledge from language corpora. LLMs achieve impressive performance on many tasks, but the robustness of their world knowledge has been questioned. Here, we ask: do LLMs acquire generalized knowledge about real-world events? Using curated sets of minimal sentence pairs (n=1215), we tested whether LLMs are more likely to generate plausible event descriptions compared to their implausible counterparts. We found that LLMs systematically distinguish possible and impossible events (The teacher bought the laptop vs. The laptop bought the teacher) but fall short of human performance when distinguishing likely and unlikely events (The nanny tutored the boy vs. The boy tutored the nanny). In follow-up analyses, we show that (i) LLM scores are driven by both plausibility and surface-level sentence features, (ii) LLMs generalize well across syntactic sentence variants (active vs passive) but less well across semantic sentence variants (synonymous sentences), (iii) some, but not all LLM deviations from ground-truth labels align with crowdsourced human judgments, and (iv) explicit event plausibility information emerges in middle LLM layers and remains high thereafter. Overall, our analyses reveal a gap in LLMs' event knowledge, highlighting their limitations as generalized knowledge bases. We conclude by speculating that the differential performance on impossible vs. unlikely events is not a temporary setback but an inherent property of LLMs, reflecting a fundamental difference between linguistic knowledge and world knowledge in intelligent systems.

The long-distance agreement, evidence for syntactic structure, is increasingly used to assess the syntactic generalization of Neural Language Models. Much work has shown that transformers are capable of high accuracy in varied agreement tasks, but the mechanisms by which the models accomplish this behavior are still not well understood. To better understand transformers' internal working, this work contrasts how they handle two superficially similar but theoretically distinct agreement phenomena: subject-verb and object-past participle agreement in French. Using probing and counterfactual analysis methods, our experiments show that i) the agreement task suffers from several confounders which partially question the conclusions drawn so far and ii) transformers handle subject-verb and object-past participle agreements in a way that is consistent with their modeling in theoretical linguistics.

Pragmatics is an essential part of communication, but it remains unclear what mechanisms underlie human pragmatic communication and whether NLP systems capture pragmatic language understanding. To investigate both these questions, we perform a fine-grained comparison of language models and humans on seven pragmatic phenomena, using zero-shot prompting on an expert-curated set of English materials. We ask whether models (1) select pragmatic interpretations of speaker utterances, (2) make similar error patterns as humans, and (3) use similar linguistic cues as humans to solve the tasks. We find that the largest models achieve high accuracy and match human error patterns: within incorrect responses, models favor the literal interpretation of an utterance over heuristic-based distractors. We also find evidence that models and humans are sensitive to similar linguistic cues. Our results suggest that even paradigmatic pragmatic phenomena may be solved without explicit representations of other agents' mental states, and that artificial models can be used to gain mechanistic insights into human pragmatic processing.

借助情境化语言模型的成功，许多研究探讨了这些模型真正学到的知识，并且在哪些情况下仍然失败。这项工作的大部分都集中在特定的NLP任务和学习成果上。很少的研究试图使模型的弱点与特定任务的弱点相结合，并专注于嵌入本身及其学习方式。在本文中，我们抓住了这一研究机会：基于理论语言见解，我们探讨了功能词的语义限制是否是学习的，以及周围环境如何影响其嵌入。我们创建合适的数据集，为LMS VIS-VIS功能单词的内部工作提供新的见解，并实施辅助视觉网络界面以进行定性分析。

语法提示有时具有自然语言的单词含义。例如，英语单词顺序规则限制了句子的单词顺序，例如“狗咀嚼骨头”，即使可以从世界知识和合理性中推断出“狗”作为代理人和“骨头”的状态。量化这种冗余的发生频率，以及冗余水平如何在类型上多样化的语言中变化，可以阐明语法的功能和演变。为此，我们在英语和俄语中进行了一个行为实验，并进行了跨语言计算分析，以测量从自然主义文本中提取的及物子句中语法线索的冗余性。从自然发生的句子中提取的主题，动词和物体（按随机顺序和形态标记）提出了英语和俄罗斯说话者（n = 484），并被要求确定哪个名词是该动作的推动者。两种语言的准确性都很高（英语约为89％，俄语为87％）。接下来，我们在类似的任务上训练了神经网络机分类器：预测主题对象三合会中的哪个名义是主题。在来自八个语言家庭的30种语言中，性能始终很高：中位准确性为87％，与人类实验中观察到的准确性相当。结论是，语法提示（例如单词顺序）对于仅在10-15％的自然句子中传达了代理和耐心是必要的。然而，他们可以（a）提供重要的冗余来源，（b）对于传达无法从单词中推断出的预期含义至关重要，包括对人类互动的描述，在这些含义中，角色通常是可逆的（例如，雷（Ray）帮助lu/ Lu帮助雷），表达了非典型的含义（例如，“骨头咀嚼狗”。）。

Language Models appear to perform poorly on quantification. We ask how badly. 'Few'-type quantifiers, as in 'few children like vegetables' might pose a particular challenge for Language Models, since the sentence components without the quantifier are likely to co-occur, and because 'few'-type quantifiers are rare. We present 960 sentences stimuli from two human neurolinguistic experiments to 22 autoregressive transformer models of differing sizes. Not only do the models perform poorly on 'few'-type quantifiers, but overall the larger the model, the worse its performance. We interpret this inverse scaling as suggesting that larger models increasingly reflect online rather than offline human processing, and argue that decreasing performance of larger models may challenge uses of Language Models as the basis for Natural Language Systems.

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.

自然语言处理的机器学习快速进步有可能改变有关人类学习语言的辩论。但是，当前人工学习者和人类的学习环境和偏见以削弱从学习模拟获得的证据的影响的方式分歧。例如，当今最有效的神经语言模型接受了典型儿童可用的语言数据量的大约一千倍。为了增加计算模型的可学习性结果的相关性，我们需要培训模型学习者，而没有比人类具有显着优势的学习者。如果合适的模型成功地获得了一些目标语言知识，则可以提供一个概念证明，即在假设的人类学习方案中可以学习目标。合理的模型学习者将使我们能够进行实验操作，以对学习环境中的变量进行因果推断，并严格测试史密斯风格的贫困声明，主张根据人类对人类的先天语言知识，基于有关可学习性的猜测。由于实用和道德的考虑因素，人类受试者将永远无法实现可比的实验，从而使模型学习者成为必不可少的资源。到目前为止，试图剥夺当前模型的不公平优势，为关键语法行为（例如可接受性判断）获得亚人类结果。但是，在我们可以合理地得出结论，语言学习需要比当前模型拥有更多的特定领域知识，我们必须首先以多模式刺激和多代理互动的形式探索非语言意见，以使学习者更有效地学习学习者来自有限的语言输入。

神经网络语言模型的最新进展表明，通过利用大规模自然语言数据中的语言关联来得出表达意义表示。这些潜在的格式塔表示已实现许多实际应用的最新性能。看来我们正处于经验得出强大而表达的可计算语义的途径。出现的一个关键问题是，仅语言数据才能使计算机能够理解有关物理世界的必要真相？必须关注这个问题，因为我们与智能机器的未来相互作用取决于我们的技术正确地表示和处理人类通常观察到的概念（对象，属性和过程）。在审查了现有协议之后，这项工作的目的是使用新颖且严格控制的推理测试探索这个问题，并突出显示哪些模型可能直接从纯语言数据中学习。

目前，自然语言理解（NLU）中最根本的两个挑战是：（a）如何以“正确”的原因确定基于深度学习的模型是否在NLU基准上得分很高；（b）了解这些原因甚至是什么。我们研究了关于两个语言“技能”的阅读理解模型的行为：核心分辨率和比较。我们为从系统中预期的推理步骤提出了一个定义，该系统将“缓慢阅读”，并将其与各种大小的贝特家族的五个模型的行为进行比较，这是通过显着分数和反事实解释观察到的。我们发现，对于比较（而不是核心），基于较大编码器的系统更有可能依靠“正确”的信息，但即使他们在概括方面也很难，表明他们仍然学习特定的词汇模式，而不是比较的一般原则。

相同上下文的可能后果可能会因我们所指的情况而异。但是，当前在自然语言处理中的研究并不集中于多种可能情况下的常识性推理。本研究通过短篇小说文字提出与候选人答案相同的结尾的多个问题来构成这项任务。我们由此产生的数据集，可能的故事，包括超过1.3k的故事文本超过4.5k的问题。我们发现，即使是目前的强训练性语言模型也很难始终如一地回答问题，这强调了无监督环境中最高的准确性（60.2％）远远落后于人类准确性（92.5％）。通过与现有数据集进行比较，我们观察到数据集中的问题包含答案选项中的最小注释伪像。此外，我们的数据集还包括需要反事实推理的示例，以及需要读者的反应和虚构信息的示例，这表明我们的数据集可以作为对未来常识性推理的未来研究的挑战性测试。

For natural language understanding (NLU) technology to be maximally useful, it must be able to process language in a way that is not exclusive to a single task, genre, or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation (GLUE) benchmark, a collection of tools for evaluating the performance of models across a diverse set of existing NLU tasks. By including tasks with limited training data, GLUE is designed to favor and encourage models that share general linguistic knowledge across tasks. GLUE also includes a hand-crafted diagnostic test suite that enables detailed linguistic analysis of models. We evaluate baselines based on current methods for transfer and representation learning and find that multi-task training on all tasks performs better than training a separate model per task. However, the low absolute performance of our best model indicates the need for improved general NLU systems.

众所周知，端到端的神经NLP体系结构很难理解，这引起了近年来为解释性建模的许多努力。模型解释的基本原则是忠诚，即，解释应准确地代表模型预测背后的推理过程。这项调查首先讨论了忠诚的定义和评估及其对解释性的意义。然后，我们通过将方法分为五类来介绍忠实解释的最新进展：相似性方法，模型内部结构的分析，基于反向传播的方法，反事实干预和自我解释模型。每个类别将通过其代表性研究，优势和缺点来说明。最后，我们从它们的共同美德和局限性方面讨论了上述所有方法，并反思未来的工作方向忠实的解释性。对于有兴趣研究可解释性的研究人员，这项调查将为该领域提供可访问且全面的概述，为进一步探索提供基础。对于希望更好地了解自己的模型的用户，该调查将是一项介绍性手册，帮助选择最合适的解释方法。

预先接受训练的语言模型的进展导致了对自然语言理解的下游任务的令人印象深刻的结果。探索预先训练的语言模型的最新工作揭示了在其上下围化表示中编码的广泛的语言属性。然而，目前尚不清楚他们是否编码对符号推理方法至关重要的语义知识。我们提出了一种用于探测预先接受训练的语言模型表示的逻辑推断的语言信息的方法。我们的探测数据集涵盖主要符号推理系统所需的语言现象列表。我们发现（i）预先接受的语言模型为推断编码几种类型的语言信息，但是还有一些类型的信息弱编码，（ii）语言模型可以通过微调有效地学习语言信息缺少语言信息。总体而言，我们的调查结果提供了逻辑推理语言模型的语言信息的洞察力，以及他们的预训练程序捕获。此外，我们已经证明了语言模型作为语义和背景知识库的潜力，用于支持符号推断方法。

大量培训数据是最先进的NLP模型高性能的主要原因之一。但是，在培训数据中，什么导致模型做出一定的预测？我们试图通过提供一种通过因果框架来描述培训数据如何影响预测的语言来回答这个问题。重要的是，我们的框架绕过了重新培训昂贵模型的需求，并使我们能够仅基于观察数据来估计因果效应。解决从验证的语言模型（PLM）中提取事实知识的问题，我们重点介绍了简单的数据统计数据，例如共发生计数，并表明这些统计数据确实会影响PLM的预测，这表明此类模型依赖于浅启发式方法。我们的因果框架和结果表明，研究数据集的重要性以及因果关系对理解NLP模型的好处。