While large pretrained language models (PLMs) demonstrate incredible fluency and performance on many natural language tasks, recent work has shown that well-performing PLMs are very sensitive to what prompts are feed into them. Even when prompts are semantically identical, language models may give very different answers. When considering safe and trustworthy deployments of PLMs we would like their outputs to be consistent under prompts that mean the same thing or convey the same intent. While some work has looked into how state-of-the-art PLMs address this need, they have been limited to only evaluating lexical equality of single- or multi-word answers and do not address consistency of generative text sequences. In order to understand consistency of PLMs under text generation settings, we develop a measure of semantic consistency that allows the comparison of open-ended text outputs. We implement several versions of this consistency metric to evaluate the performance of a number of PLMs on paraphrased versions of questions in the TruthfulQA dataset, we find that our proposed metrics are considerably more consistent than traditional metrics embodying lexical consistency, and also correlate with human evaluation of output consistency to a higher degree.

语言理解的概率模型是可解释和结构化的，例如隐喻理解的模型描述了有关潜在主题和特征的推论。但是，这些模型是为特定任务手动设计的。大型语言模型（LLMS）可以通过内在的学习来执行许多任务，但它们缺乏概率模型的清晰结构。在本文中，我们使用经过思考的提示将概率模型的结构引入LLMS。这些提示导致该模型推断潜在变量和有关其关系的理由，以选择隐喻的适当释义。所选择的潜在变量和关系是由认知心理学理解理论得出的。我们将这些提示应用于GPT-3的两个最大版本，并表明它们可以改善释义选择。

知识密集型任务，例如开放域问题答案（QA），需要访问大量的世界知识或领域知识。知识密集型任务的一种常见方法是采用检索到阅读的管道，该管道首先从诸如Wikipedia之类的外部语料库中检索少数相关的上下文文档，然后预测在检索文档的条件下得到答案。在本文中，我们提出了一种新的观点，可以通过用大型语言模型生成器代替文档检索器来解决知识密集型任务。我们称我们的方法生成-Read Read（GenRead），该方法首先提示大型语言模型根据给定问题生成上下文文档，然后读取生成的文档以产生最终答案。此外，我们提出了一种基于聚类的提示方法，该方法选择了不同的提示，从而产生了涵盖不同观点的生成文档，从而更好地回忆了可接受的答案。我们对三个不同的知识密集任务进行了广泛的实验，包括开放域质量检查，事实检查和对话系统。值得注意的是，GenRead在Triviaqa和WebQ上实现了71.6和54.4的精确匹配分数，显着超过了最先进的检索到+4.0和+3.9的最先进的dpr-fid，而无需从任何外部知识源中检索任何文档。最后，我们证明可以通过结合检索和生成来进一步提高模型性能。

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.

最近的研究利用了先进的生成语言模型来生成自然语言解释（NLE），以了解某个文本可能会令人讨厌。我们提出了一系列解释提示方法，灵感来自思想链研究\ cite {wei2022chain}，以生成高质量的nle，以实现隐式仇恨言论。我们基于选定的主流预训练的语言模型（PLM）建立基准，包括GPT-2，GPT-NEO，OPT，T5和BART，以及来自词汇，语义和忠实方面的各种评估指标。为了进一步评估人类感知产生的NLE的质量，我们雇用人类注释者来评估生成的NLE的信息性和清晰度。然后，我们检查哪种自动评估指标可以最好地与人类通知的信息性和清晰度度量分数相关。

GPT-3和Palm等大型语言模型在几次学习中表现出色。但是，他们仍然在推理任务（例如算术基准GSM8K）上挣扎。最近的进步故意指导语言模型在产生最终答案之前生成一系列推理步骤，从而成功地将GSM8K基准从17.9％提高到58.1％，以解决问题的解决率。在本文中，我们提出了一种新的方法，即多样化的方法（关于推理步骤的多样化验证者），以进一步提高其推理能力。多样性首先探索不同的提示，以增强推理路径的多样性。其次，Diverse介绍了一个验证者，以区分好的答案和不良答案，从而获得更好的权重投票。最后，多样性验证每个步骤的正确性，而不是整体上的所有步骤。我们使用最新的语言型号Davinci-002进行广泛的实验，并证明多样化可以在八分之六的推理基准中实现新的最先进的性能（例如，GSM8K 74.4％至83.2％），超过棕榈具有540B参数的模型。

公开可用的大型预磨语删除媒介（LMS）生成具有显着质量的文本，但仅从左右依次顺序地。因此，它们不会立即适用于打破单向假设的生成任务，例如释放或文本缺陷，需要特定于特定的监督。在本文中，我们呈现反射解码，这是一种新型无监督算法，其允许直接向非顺序任务应用单向LMS。我们的2步方法不需要监督甚至并行对象，只有两个离心的预磨损LMS相反的方向：向前和向后。首先，在上下文化步骤中，我们使用LMS生成过去和未来环境的集合，该上下文共同捕获输入（例如，索引源句）。其次，在反射步骤中，我们在这些“上下文集合”中的条件，生成与它们兼容的输出。综合经验结果表明，反思解码优于涉及释义和绑架文本缺陷的强烈无监督的基线，显着缩小无监督和监督方法之间的差距。反射解码超越了各种度量的多个监督基线，包括人为评估。

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。

The recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from original work. However, the role of large autoregressive transformers in generating machine-paraphrased plagiarism and their detection is still developing in the literature. This work explores T5 and GPT-3 for machine-paraphrase generation on scientific articles from arXiv, student theses, and Wikipedia. We evaluate the detection performance of six automated solutions and one commercial plagiarism detection software and perform a human study with 105 participants regarding their detection performance and the quality of generated examples. Our results suggest that large models can rewrite text humans have difficulty identifying as machine-paraphrased (53% mean acc.). Human experts rate the quality of paraphrases generated by GPT-3 as high as original texts (clarity 4.0/5, fluency 4.2/5, coherence 3.8/5). The best-performing detection model (GPT-3) achieves a 66% F1-score in detecting paraphrases.

我们表明，GPT-3模型可以学会在不使用模型逻辑的情况下以自然语言来表达其自然语言答案的不确定性。当提出问题时，该模型同时产生答案和信心水平（例如“ 90％的置信度”或“高信心”）。这些级别映射到经过校准的概率。该模型在分配转移下还保持适度的校准，并且对自己的答案中的不确定性敏感，而不是模仿人类的例子。据我们所知，这是第一次证明模型对其自然语言的答案表达了校准的不确定性。为了测试校准，我们介绍了校准任务套件。我们比较了用单词（“语言概率”）表达的不确定性的校准与从模型逻辑提取的不确定性。两种不确定性都能够在分布变化下概括校准。我们还提供了证据表明，GPT-3概括校准的能力取决于预先训练的潜在表示，这些表征与其答案上的认知不确定性相关。

Large-scale generative models show an impressive ability to perform a wide range of Natural Language Processing (NLP) tasks using in-context learning, where a few examples are used to describe a task to the model. For Machine Translation (MT), these examples are typically randomly sampled from the development dataset with a similar distribution as the evaluation set. However, it is unclear how the choice of these in-context examples and their ordering impacts the output translation quality. In this work, we aim to understand the properties of good in-context examples for MT in both in-domain and out-of-domain settings. We show that the translation quality and the domain of the in-context examples matter and that 1-shot noisy unrelated example can have a catastrophic impact on output quality. While concatenating multiple random examples reduces the effect of noise, a single good prompt optimized to maximize translation quality on the development dataset can elicit learned information from the pre-trained language model. Adding similar examples based on an n-gram overlap with the test source significantly and consistently improves the translation quality of the outputs, outperforming a strong kNN-MT baseline in 2 out of 4 out-of-domain datasets.

Explaining the black-box predictions of NLP models naturally and accurately is an important open problem in natural language generation. These free-text explanations are expected to contain sufficient and carefully-selected evidence to form supportive arguments for predictions. Due to the superior generative capacity of large pretrained language models, recent work built on prompt engineering enables explanation generation without specific training. However, explanation generated through single-pass prompting often lacks sufficiency and conciseness. To address this problem, we develop an information bottleneck method EIB to produce refined explanations that are sufficient and concise. Our approach regenerates the free-text explanation by polishing the single-pass output from the pretrained language model but retaining the information that supports the contents being explained. Experiments on two out-of-domain tasks verify the effectiveness of EIB through automatic evaluation and thoroughly-conducted human evaluation.

Fine-tuned language models use greedy decoding to answer reading comprehension questions with relative success. However, this approach does not ensure that the answer is a span in the given passage, nor does it guarantee that it is the most probable one. Does greedy decoding actually perform worse than an algorithm that does adhere to these properties? To study the performance and optimality of greedy decoding, we present exact-extract, a decoding algorithm that efficiently finds the most probable answer span in the context. We compare the performance of T5 with both decoding algorithms on zero-shot and few-shot extractive question answering. When no training examples are available, exact-extract significantly outperforms greedy decoding. However, greedy decoding quickly converges towards the performance of exact-extract with the introduction of a few training examples, becoming more extractive and increasingly likelier to generate the most probable span as the training set grows. We also show that self-supervised training can bias the model towards extractive behavior, increasing performance in the zero-shot setting without resorting to annotated examples. Overall, our results suggest that pretrained language models are so good at adapting to extractive question answering, that it is often enough to fine-tune on a small training set for the greedy algorithm to emulate the optimal decoding strategy.

诸如GPT-3之类的语言模型在研究界引起了愤怒。一些研究发现，GPT-3具有一些创造力，并犯了与人类行为相提并论的错误。本文回答了一个相关的问题：谁是GPT-3？我们为GPT-3管理了两个经过验证的测量工具，以评估其个性，其所持值和自我报告的人口统计。我们的结果表明，GPT -3在人格中与人类样本的分数相似，并且在提供模型响应记忆时 - 根据其所持值。我们提供了对GPT-3模型的心理评估的第一个证据，从而增加了我们对GPT-3模型的理解。我们对未来研究的建议结束，使社会科学更接近语言模型，反之亦然。

人工推理通常可以理解为两个系统之间的相互作用：直观和关联（“系统1”）和审议和逻辑（“系统2”）。神经序列模型 - 在执行复杂，结构化任务时越来越成功 - 表现出系统1的优点和故障模式：它们是快速和学习数据的模式，但通常不一致和不连贯。在这项工作中，我们通过添加系统2-Inspired逻辑推理，寻求一种轻量级，无培训的手段来改善现有系统1样序列模型。我们探讨了该主题的几种变体，其中通过符号推理模块检查来自神经序列模型的候选几代，可以通过符号推理模块来接受或拒绝几代人。我们的方法使用神经推理来介导神经系统1和逻辑系统2.导致强大的故事生成和接地的指示，表明这种方法可以增加神经基代的一致性和准确性。

Children acquiring English make systematic errors on subject control sentences even after they have reached near-adult competence (C. Chomsky, 1969), possibly due to heuristics based on semantic roles (Maratsos, 1974). Given the advanced fluency of large generative language models, we ask whether model outputs are consistent with these heuristics, and to what degree different models are consistent with each other. We find that models can be categorized by behavior into three separate groups, with broad differences between the groups. The outputs of models in the largest group are consistent with positional heuristics that succeed on subject control but fail on object control. This result is surprising, given that object control is orders of magnitude more frequent in the text data used to train such models. We examine to what degree the models are sensitive to prompting with agent-patient information, finding that raising the salience of agent and patient relations results in significant changes in the outputs of most models. Based on this observation, we leverage an existing dataset of semantic proto-role annotations (White, et al. 2020) to explore the connections between control and labeling event participants with properties typically associated with agents and patients.

最近的研究表明，理性或逐步思想链可用于改善多步推理任务的性能。我们重新考虑了理由的提示，提示了几次射击中的内部学习学习，其中（输入 - >输出）提示将扩展到（输入，理由 - >输出）提示。对于以理由为提示的提示，我们证明了现有的方法（依赖手动及时工程）如何受到可能损害绩效的次级理由。为了减轻这种脆弱性，我们提出了一个统一的授权合奏的统一框架，在该框架中，我们将输出空间中的理由抽样确定为可鲁棒提高性能的关键组成部分。该框架是一般的，可以轻松地扩展到常见的自然语言处理任务，即使传统上不利于中间步骤的任务，例如问题回答，单词感官歧义和情感分析。我们证明，与现有的提示方法相比，以理由为原理的合奏获得了更准确和可解释的结果 - 包括标准提示，没有理由和基于理由的链链链，同时通过相关理性同时提高了模型预测的解释性。

Current large language models can perform reasonably well on complex tasks that require step-by-step reasoning with few-shot learning. Are these models applying reasoning skills they have learnt during pre-training and reason outside of their training context, or are they simply memorizing their training corpus at finer granularity and have learnt to better understand their context? To tease apart these possibilities, we introduce ALERT, a benchmark and suite of analyses for assessing language models' reasoning ability comparing pre-trained and finetuned models on complex tasks that require reasoning skills to solve. ALERT provides a test bed to asses any language model on fine-grained reasoning skills, which spans over 20 datasets and covers 10 different reasoning skills. We leverage ALERT to further investigate the role of finetuning. With extensive empirical analysis we find that language models learn more reasoning skills such as textual entailment, abductive reasoning, and analogical reasoning during finetuning stage compared to pretraining state. We also find that when language models are finetuned they tend to overfit to the prompt template, which hurts the robustness of models causing generalization problems.

预测任务标签和为其预测生成自由文本阐述的自律化模型可以实现与NLP系统更直观的交互。然而，这些模型目前正在接受大量人为的自由文本解释，每个任务都会阻碍更广泛的使用。我们建议使用少数培训例子研究更现实的自律化建立。我们出示2月 - 一个标准化的四个现有英语数据集和相关指标。我们通过2月份广泛探索自然语言提示来确定正确的提示方法。然后，通过使用此提示并缩放模型大小，我们证明了几次拍摄自合合理化的进展。我们展示了这项任务的完善房间仍然有充足的改进空间：人类注册人评估的生成解释的平均合理性最多为51％，而人类解释的合理性是76％。我们希望2月份与我们的拟议方法一起促使社区承担几次拍摄的自我合理化挑战。

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.