语言模型（LMS）已被证明在各种下游应用程序中很有用，例如摘要，翻译，问答和文本分类。由于它们可以存储的大量信息，LMS正在成为人工智能中越来越重要的工具。在这项工作中，我们提出了道具（提示为探测），该道具利用GPT-3（最初由OpenAI在2020年提出的大型语言模型）来执行知识基础构建任务（KBC）。 Prop实施了一种多步骤方法，该方法结合了各种提示技术来实现这一目标。我们的结果表明，手动提示策划是必不可少的，必须鼓励LM给出可变长度的答案集，特别是包括空的答案集，True/False问题是提高LM生成的建议精度的有用设备。 LM的大小是至关重要的因素，并且实体字典别名提高了LM评分。我们的评估研究表明，这些提出的技术可以大大提高最终预测的质量：Prop赢得了LM-KBC竞争的轨道2，表现优于基线36.4个百分点。我们的实施可在https://github.com/hemile/iswc-challenge上获得。

我们在ISWC 2022上对知识图模型的知识形象人群提出了一个用于语言模型的系统，该系统对知识库构建（LM-KBC）挑战进行了评估。我们的系统涉及特定于任务的预培训以改善蒙版的LM表示。对象令牌，促使分解候选对象以及其他高质量检索的方法。我们的系统是基于BERT LM的LM-KBC挑战赛曲目1的获胜者；它在挑战的隐藏测试集中获得了55.0％的F-1得分。

The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining. Reformulating tasks as fillin-the-blanks problems (e.g., cloze tests) is a natural approach for gauging such knowledge, however, its usage is limited by the manual effort and guesswork required to write suitable prompts. To address this, we develop AUTOPROMPT, an automated method to create prompts for a diverse set of tasks, based on a gradient-guided search. Using AUTO-PROMPT, we show that masked language models (MLMs) have an inherent capability to perform sentiment analysis and natural language inference without additional parameters or finetuning, sometimes achieving performance on par with recent state-of-the-art supervised models. We also show that our prompts elicit more accurate factual knowledge from MLMs than the manually created prompts on the LAMA benchmark, and that MLMs can be used as relation extractors more effectively than supervised relation extraction models. These results demonstrate that automatically generated prompts are a viable parameter-free alternative to existing probing methods, and as pretrained LMs become more sophisticated and capable, potentially a replacement for finetuning.

符号知识图（kgs）是通过昂贵的人众包或特定于域特异性的复杂信息提取管道来构建的。诸如BERT之类的新兴大型语言模型（LMS）已显示出隐式编码的大量知识，可以使用正确设计的提示来查询。但是，与明确的公斤相比，黑盒LMS中的知识通常很难访问或编辑，并且缺乏解释性。在这项工作中，我们旨在从LMS收获符号KG，这是一个由神经LMS的灵活性和可扩展性增强的自动kg构造的新框架。与通常依赖大型人类注释的数据或现有大量KG的先前作品相比，我们的方法仅需要对关系的最小定义作为输入，因此适合于以前无法提取有关丰富新关系的知识。该方法会自动生成多样化的提示，并在给定的LM内执行有效的知识搜索，以进行一致和广泛的输出。与以前的方法相比，使用我们的方法收获的知识要准确得多，如自动和人类评估所示。结果，我们源于多元化的LMS，一个新的KG家族（例如Bertnet和Robertanet），其中包含一套更丰富的常识关系，包括复杂的关系（例如，A对B的能力，但不擅长B”）人类注销的kg（例如概念网）。此外，由此产生的kg也是解释各自的源LMS的工具，从而导致对不同LMS不同知识能力的新见解。

petroni等。 （2019）证明，可以通过将它们表达为冻结式提示并将模型的预测准确性解释为下限，作为其编码的事实信息量的较低限制，从预先接收的语言模型中检索世界事实。随后的工作已经尝试通过搜索更好的提示来缩回估计，使用不相交的事实作为培训数据。在这项工作中，我们制作两个互补贡献，以更好地了解这些事实探测技术。首先，我们提出了OptiPrompt，一种新颖的和有效的方法，直接在连续嵌入空间中优化。我们发现这种简单的方法能够预测喇嘛基准中的额外6.4％的事实。其次，我们提出了一个更重要的问题：我们真的可以将这些探测结果解释为下限吗？这些提示搜索方法是否有可能从培训数据中学习？我们发现，有些令人惊讶的是，这些方法使用的培训数据包含了潜在的事实分布的某些规则，以及所有现有的提示方法，包括我们的方法，可以利用它们以获得更好的事实预测。我们开展一系列控制实验来解除“学习”从“学习召回”，提供了更详细的图片，不同的提示可以揭示关于预先接受的语言模型。

Recent work has presented intriguing results examining the knowledge contained in language models (LM) by having the LM fill in the blanks of prompts such as "Obama is a by profession". These prompts are usually manually created, and quite possibly suboptimal; another prompt such as "Obama worked as a " may result in more accurately predicting the correct profession. Because of this, given an inappropriate prompt, we might fail to retrieve facts that the LM does know, and thus any given prompt only provides a lower bound estimate of the knowledge contained in an LM. In this paper, we attempt to more accurately estimate the knowledge contained in LMs by automatically discovering better prompts to use in this querying process. Specifically, we propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts, as well as ensemble methods to combine answers from different prompts. Extensive experiments on the LAMA benchmark for extracting relational knowledge from LMs demonstrate that our methods can improve accuracy from 31.1% to 39.6%, providing a tighter lower bound on what LMs know. We have released the code and the resulting LM Prompt And Query Archive (LPAQA) at https://github. com/jzbjyb/LPAQA.1 Some models we use in this paper, e.g. BERT (Devlin et al., 2019), are bi-directional, and do not directly define probability distribution over text, which is the underlying definition of an LM. Nonetheless, we call them LMs for simplicity.

大型基于变压器的预训练的语言模型在各种知识密集的任务上取得了令人印象深刻的表现，并可以在其参数中捕获事实知识。我们认为，考虑到不断增长的知识和资源需求，在模型参数中存储大量知识是亚最佳选择。我们认为，更有效的替代方法是向模型提供对上下文相关的结构化知识的明确访问，并训练它以使用该知识。我们提出了LM核 - 实现这一目标的一般框架 - 允许从外部知识源对语言模型培训的\ textit {解耦}，并允许后者更新而不会影响已经训练的模型。实验结果表明，LM核心获得外部知识，在知识探索任务上的最先进的知识增强语言模型中实现了重要而强大的优于性能。可以有效处理知识更新；并在两个下游任务上表现良好。我们还提出了一个彻底的错误分析，突出了LM核的成功和失败。

学术知识图（KGS）提供了代表科学出版物编码的知识的丰富的结构化信息来源。随着出版的科学文学的庞大，包括描述科学概念的过多的非均匀实体和关系，这些公斤本质上是不完整的。我们呈现Exbert，一种利用预先训练的变压器语言模型来执行学术知识图形完成的方法。我们将知识图形的三元组模型为文本并执行三重分类（即，属于KG或不属于KG）。评估表明，在三重分类，链路预测和关系预测的任务中，Exbert在三个学术kg完成数据集中表现出其他基线。此外，我们将两个学术数据集作为研究界的资源，从公共公共公报和在线资源中收集。

Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. Whilst learning linguistic knowledge, these models may also be storing relational knowledge present in the training data, and may be able to answer queries structured as "fillin-the-blank" cloze statements. Language models have many advantages over structured knowledge bases: they require no schema engineering, allow practitioners to query about an open class of relations, are easy to extend to more data, and require no human supervision to train. We present an in-depth analysis of the relational knowledge already present (without fine-tuning) in a wide range of state-of-theart pretrained language models. We find that (i) without fine-tuning, BERT contains relational knowledge competitive with traditional NLP methods that have some access to oracle knowledge, (ii) BERT also does remarkably well on open-domain question answering against a supervised baseline, and (iii) certain types of factual knowledge are learned much more readily than others by standard language model pretraining approaches. The surprisingly strong ability of these models to recall factual knowledge without any fine-tuning demonstrates their potential as unsupervised open-domain QA systems. The code to reproduce our analysis is available at https: //github.com/facebookresearch/LAMA.

现在，通过复杂的神经网络模型（例如蒙版的神经语言模型（MNLM））学习了许多上下文化的单词表示形式，这些模型由巨大的神经网络结构组成，并经过训练以恢复蒙面文本。这样的表示表明在某些阅读理解（RC）任务中表现出超人的表现，这些任务在给出问题的上下文中提取了适当的答案。但是，由于许多模型参数，确定在MNLM中训练的详细知识是具有挑战性的。本文提供了有关MNLMS中包含的常识性知识的新见解和经验分析。首先，我们使用诊断测试来评估常识性知识是否在MNLMS中进行了适当的培训。我们观察到，在MNLMS中没有适当训练很多常识性知识，并且MNLMS并不经常准确地理解关系的语义含义。此外，我们发现基于MNLM的RC模型仍然容易受到需要常识知识的语义变化的影响。最后，我们发现了未经训练的知识的基本原因。我们进一步建议，利用外常识性知识存储库可以是一个有效的解决方案。我们说明了通过在受控实验中以外常识性知识存储库来丰富文本的经文，以克服基于MNLM的RC模型的局限性的可能性。

Relation extraction (RE) is a sub-discipline of information extraction (IE) which focuses on the prediction of a relational predicate from a natural-language input unit (such as a sentence, a clause, or even a short paragraph consisting of multiple sentences and/or clauses). Together with named-entity recognition (NER) and disambiguation (NED), RE forms the basis for many advanced IE tasks such as knowledge-base (KB) population and verification. In this work, we explore how recent approaches for open information extraction (OpenIE) may help to improve the task of RE by encoding structured information about the sentences' principal units, such as subjects, objects, verbal phrases, and adverbials, into various forms of vectorized (and hence unstructured) representations of the sentences. Our main conjecture is that the decomposition of long and possibly convoluted sentences into multiple smaller clauses via OpenIE even helps to fine-tune context-sensitive language models such as BERT (and its plethora of variants) for RE. Our experiments over two annotated corpora, KnowledgeNet and FewRel, demonstrate the improved accuracy of our enriched models compared to existing RE approaches. Our best results reach 92% and 71% of F1 score for KnowledgeNet and FewRel, respectively, proving the effectiveness of our approach on competitive benchmarks.

关于时间知识图（TKGQA）的问题回答最近发现兴趣越来越大。 TKGQA需要时间推理技术来从时间知识库中提取相关信息。唯一现有的TKGQA数据集，即cronquestions，由基于固定时间段内的事实组成，其中跨越同一时期的时间知识图（TKG）可以完全使用用于答案推断，允许使用TKGQA模型。即将根据过去事实回答问题的未来知识。但是，在现实世界的情况下，鉴于到目前为止的知识也很常见，我们希望TKGQA系统回答询问未来的问题。随着人类不断寻求未来计划，建立用于回答此类预测问题的TKGQA系统很重要。然而，这在先前的研究中仍未得到探索。在本文中，我们提出了一个新的任务：关于时间知识图的预测问题。我们还为此任务提出了一个大规模的TKGQA基准数据集，即预测。它包括三种类型的问题，即实体预测，不是和事实推理问题。对于我们数据集中的每个预测问题，QA模型只能在给定问题中注释的时间戳以进行答案推理之前访问TKG信息。我们发现，最先进的TKGQA方法在预测问题上的表现较差，并且他们无法回答不是问题和事实推理问题。为此，我们提出了一种TKGQA模型预测，该模型采用TKG预测模块进行未来推断，以回答所有三种类型的问题。实验结果表明，预测到实体预测问题的最新方法优于最近的TKGQA方法，并且在回答其他两种类型的问题方面也显示出很大的有效性。

Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge. This paper aims to understand LMs' strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments of 10 models and 4 augmentation methods on PopQA, our new open-domain QA dataset with 14k questions. We find that LMs struggle with less popular factual knowledge, and that scaling fails to appreciably improve memorization of factual knowledge in the tail. We then show that retrieval-augmented LMs largely outperform orders of magnitude larger LMs, while unassisted LMs remain competitive in questions about high-popularity entities. Based on those findings, we devise a simple, yet effective, method for powerful and efficient retrieval-augmented LMs, which retrieves non-parametric memories only when necessary. Experimental results show that this significantly improves models' performance while reducing the inference costs.

Natural Language Processing (NLP) has been revolutionized by the use of Pre-trained Language Models (PLMs) such as BERT. Despite setting new records in nearly every NLP task, PLMs still face a number of challenges including poor interpretability, weak reasoning capability, and the need for a lot of expensive annotated data when applied to downstream tasks. By integrating external knowledge into PLMs, \textit{\underline{K}nowledge-\underline{E}nhanced \underline{P}re-trained \underline{L}anguage \underline{M}odels} (KEPLMs) have the potential to overcome the above-mentioned limitations. In this paper, we examine KEPLMs systematically through a series of studies. Specifically, we outline the common types and different formats of knowledge to be integrated into KEPLMs, detail the existing methods for building and evaluating KEPLMS, present the applications of KEPLMs in downstream tasks, and discuss the future research directions. Researchers will benefit from this survey by gaining a quick and comprehensive overview of the latest developments in this field.

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。

完成知识三胞胎的任务具有广泛的下游应用程序。结构和语义信息在知识图完成中起着重要作用。与以前依靠知识图的结构或语义的方法不同，我们建议将语义共同嵌入知识三胞胎的自然语言描述及其结构信息。我们的方法通过对概率结构化损失进行微调预训练的语言模型来嵌入完成任务的知识图，其中语言模型的正向通过捕获语义和损失重建结构。我们对各种知识图基准的广泛实验证明了我们方法的最新性能。我们还表明，由于语义的更好使用，我们的方法可以显着提高低资源制度的性能。代码和数据集可在https://github.com/pkusjh/lass上找到。

The strong few-shot in-context learning capability of large pre-trained language models (PLMs) such as GPT-3 is highly appealing for application domains such as biomedicine, which feature high and diverse demands of language technologies but also high data annotation costs. In this paper, we present the first systematic and comprehensive study to compare the few-shot performance of GPT-3 in-context learning with fine-tuning smaller (i.e., BERT-sized) PLMs on two highly representative biomedical information extraction tasks, named entity recognition and relation extraction. We follow the true few-shot setting to avoid overestimating models' few-shot performance by model selection over a large validation set. We also optimize GPT-3's performance with known techniques such as contextual calibration and dynamic in-context example retrieval. However, our results show that GPT-3 still significantly underperforms compared to simply fine-tuning a smaller PLM. In addition, GPT-3 in-context learning also yields smaller gains in accuracy when more training data becomes available. Our in-depth analyses further reveal issues of the in-context learning setting that may be detrimental to information extraction tasks in general. Given the high cost of experimenting with GPT-3, we hope our study provides guidance for biomedical researchers and practitioners towards more promising directions such as fine-tuning small PLMs.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

在本文中，我们描述了我们参与Case-2022的子任务1，即与休闲新闻语料库的事件因果关系识别。我们通过在少数带注释的示例（即几次配置）上利用一组简单但互补的技术来解决因果关系识别（CRI）任务。我们遵循一种基于迅速的预测方法，用于微调LMS，其中CRI任务被视为掩盖语言建模问题（MLM）。这种方法允许LMS在MLM问题上进行本地预先训练，可以直接生成对CRI特异性提示的文本响应。我们将此方法的性能与在整个数据集中训练的集合技术进行比较。我们表现​​最佳的提交仅接受了每班256个实例，整个数据集的一小部分培训，但能够获得第二好的精度（0.82），第三好的精度（0.82）和F1得分。 （0.85）非常接近获胜者团队（0.86）的报道。

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.