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.

We present Pre-trained Machine Reader (PMR), a novel method to retrofit Pre-trained Language Models (PLMs) into Machine Reading Comprehension (MRC) models without acquiring labeled data. PMR is capable of resolving the discrepancy between model pre-training and downstream fine-tuning of existing PLMs, and provides a unified solver for tackling various extraction tasks. To achieve this, we construct a large volume of general-purpose and high-quality MRC-style training data with the help of Wikipedia hyperlinks and design a Wiki Anchor Extraction task to guide the MRC-style pre-training process. Although conceptually simple, PMR is particularly effective in solving extraction tasks including Extractive Question Answering and Named Entity Recognition, where it shows tremendous improvements over previous approaches especially under low-resource settings. Moreover, viewing sequence classification task as a special case of extraction task in our MRC formulation, PMR is even capable to extract high-quality rationales to explain the classification process, providing more explainability of the predictions.

A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes. However, roadblocks have included dataset shift from the general domain and a lack of public clinical corpora and annotations. In this work, we show that large language models, such as InstructGPT, perform well at zero- and few-shot information extraction from clinical text despite not being trained specifically for the clinical domain. Whereas text classification and generation performance have already been studied extensively in such models, here we additionally demonstrate how to leverage them to tackle a diverse set of NLP tasks which require more structured outputs, including span identification, token-level sequence classification, and relation extraction. Further, due to the dearth of available data to evaluate these systems, we introduce new datasets for benchmarking few-shot clinical information extraction based on a manual re-annotation of the CASI dataset for new tasks. On the clinical extraction tasks we studied, the GPT-3 systems significantly outperform existing zero- and few-shot baselines.

Two key obstacles in biomedical relation extraction (RE) are the scarcity of annotations and the prevalence of instances without explicitly pre-defined labels due to low annotation coverage. Existing approaches, which treat biomedical RE as a multi-class classification task, often result in poor generalization in low-resource settings and do not have the ability to make selective prediction on unknown cases but give a guess from seen relations, hindering the applicability of those approaches. We present NBR, which converts biomedical RE as natural language inference formulation through indirect supervision. By converting relations to natural language hypotheses, NBR is capable of exploiting semantic cues to alleviate annotation scarcity. By incorporating a ranking-based loss that implicitly calibrates abstinent instances, NBR learns a clearer decision boundary and is instructed to abstain on uncertain instances. Extensive experiments on three widely-used biomedical RE benchmarks, namely ChemProt, DDI and GAD, verify the effectiveness of NBR in both full-set and low-resource regimes. Our analysis demonstrates that indirect supervision benefits biomedical RE even when a domain gap exists, and combining NLI knowledge with biomedical knowledge leads to the best performance gains.

最近的自然语言理解进展（NLU）已经被驱动，部分是由胶水，超级格，小队等的基准。事实上，许多NLU模型现在在许多任务中匹配或超过“人类水平”性能这些基准。然而，大多数这些基准测试都提供模型访问相对大量的标记数据进行培训。因此，该模型提供了比人类所需的更多数据，以实现强大的性能。这有动机侧重于侧重于改善NLU模型的少量学习性能。然而，缺乏少量射门的标准化评估基准，导致不同纸张中的不同实验设置。为了帮助加速这一工作的工作，我们介绍了线索（受限制的语言理解评估标准），这是评估NLU模型的几次拍摄学习功能的基准。我们证明，虽然最近的模型在获得大量标记数据时达到人类性能，但对于大多数任务，少量拍摄设置中的性能存在巨大差距。我们还展示了几个拍摄设置中替代模型家族和适应技术之间的差异。最后，我们讨论了在设计实验设置时讨论了评估真实少量学习绩效的实验设置，并提出了统一的标准化方法，以获得少量学习评估。我们的目标是鼓励对NLU模型的研究，可以概括为具有少数示例的新任务。线索的代码和数据可以在https://github.com/microsoft/clues提供。

命名实体识别（ner）是从文本中提取特定类型的命名实体的任务。当前的NER模型往往依赖于人类注释的数据集，要求在目标领域和实体上广泛参与专业知识。这项工作介绍了一个询问生成的方法，它通过询问反映实体类型的需求的简单自然语言问题来自动生成NER数据集（例如，哪种疾病？）到开放式域问题应答系统。不使用任何域中资源（即，培训句子，标签或域名词典），我们的模型在我们生成的数据集上仅培训了，这在很大程度上超过了四个不同域的六个基准测试的弱势监督模型。令人惊讶的是，在NCBI疾病中，我们的模型达到75.5 F1得分，甚至优于以前的最佳弱监督模型4.1 F1得分，它利用域专家提供的丰富的域名词典。制定具有自然语言的NER的需求，也允许我们为诸如奖项等细粒度实体类型构建NER模型，其中我们的模型甚至优于完全监督模型。在三个少量的NER基准测试中，我们的模型实现了新的最先进的性能。

How can we extend a pre-trained model to many language understanding tasks, without labeled or additional unlabeled data? Pre-trained language models (PLMs) have been effective for a wide range of NLP tasks. However, existing approaches either require fine-tuning on downstream labeled datasets or manually constructing proper prompts. In this paper, we propose nonparametric prompting PLM (NPPrompt) for fully zero-shot language understanding. Unlike previous methods, NPPrompt uses only pre-trained language models and does not require any labeled data or additional raw corpus for further fine-tuning, nor does it rely on humans to construct a comprehensive set of prompt label words. We evaluate NPPrompt against previous major few-shot and zero-shot learning methods on diverse NLP tasks: including text classification, text entailment, similar text retrieval, and paraphrasing. Experimental results demonstrate that our NPPrompt outperforms the previous best fully zero-shot method by big margins, with absolute gains of 12.8% in accuracy on text classification and 18.9% on the GLUE benchmark.

Pre-trained language models (PLMs) have exhibited remarkable few-shot learning capabilities when provided a few examples in a natural language prompt as demonstrations of test instances, i.e., in-context learning. However, the performance of in-context learning is susceptible to the choice of prompt format, training examples and the ordering of the training examples. In this paper, we propose a novel nearest-neighbor calibration framework for in-context learning to ease this issue. It is inspired by a phenomenon that the in-context learning paradigm produces incorrect labels when inferring training instances, which provides a useful supervised signal to calibrate predictions. Thus, our method directly augments the predictions with a $k$-nearest-neighbor ($k$NN) classifier over a datastore of cached few-shot instance representations obtained by PLMs and their corresponding labels. Then adaptive neighbor selection and feature regularization modules are introduced to make full use of a few support instances to reduce the $k$NN retrieval noise. Experiments on various few-shot text classification tasks demonstrate that our method significantly improves in-context learning, while even achieving comparable performance with state-of-the-art tuning-based approaches in some sentiment analysis tasks.

The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context. Inspired by their findings, we study few-shot learning in a more practical scenario, where we use smaller language models for which fine-tuning is computationally efficient. We present LM-BFF-better few-shot fine-tuning of language models 1 -a suite of simple and complementary techniques for finetuning language models on a small number of annotated examples. Our approach includes (1) prompt-based fine-tuning together with a novel pipeline for automating prompt generation; and (2) a refined strategy for dynamically and selectively incorporating demonstrations into each context. Finally, we present a systematic evaluation for analyzing few-shot performance on a range of NLP tasks, including classification and regression. Our experiments demonstrate that our methods combine to dramatically outperform standard fine-tuning procedures in this low resource setting, achieving up to 30% absolute improvement, and 11% on average across all tasks. Our approach makes minimal assumptions on task resources and domain expertise, and hence constitutes a strong task-agnostic method for few-shot learning. 2 * The first two authors contributed equally. 1 Alternatively, language models' best friends forever. 2 Our implementation is publicly available at https:// github.com/princeton-nlp/LM-BFF.

迅速的学习方法通​​过诱导更好的几次表现，在他们仍然遵循基于参数的学习范式的同时，引起了自然语言处理的波动。学习中的遗忘和死记硬背的记忆问题可能会遇到不稳定的概括问题。具体而言，香草及时的学习可能难以利用死记硬背的非典型实例，在完全监督的培训或过度贴身模式的情况下使用低射击数据。为了减轻此类局限性，我们以将知识从记忆中解耦的动机发展为有助于模型在概括和记忆之间取得平衡。与香草及时学习相反，重新启动构造了培训实例中的开放式知识店，并在输入，培训和推理过程中实现检索机制，从而使该模型能够从培训语料库中检索相关环境作为能力为提示增强。广泛的实验表明，Retroppt可以在几次射击和零拍设置中获得更好的性能。此外，我们进一步说明，我们提出的撤退可以通过新数据集获得更好的概括能力。对记忆的详细分析确实显示逆转可以减少语言模型对记忆的依赖；因此，改善下游任务的概括。

GPT-3 (Generative Pre-trained Transformer 3) is a large-scale autoregressive language model developed by OpenAI, which has demonstrated impressive few-shot performance on a wide range of natural language processing (NLP) tasks. Hence, an intuitive application is to use it for data annotation. In this paper, we investigate whether GPT-3 can be used as a good data annotator for NLP tasks. Data annotation is the process of labeling data that could be used to train machine learning models. It is a crucial step in the development of NLP systems, as it allows the model to learn the relationship between the input data and the desired output. Given the impressive language capabilities of GPT-3, it is natural to wonder whether it can be used to effectively annotate data for NLP tasks. In this paper, we evaluate the performance of GPT-3 as a data annotator by comparing it with traditional data annotation methods and analyzing its output on a range of tasks. Through this analysis, we aim to provide insight into the potential of GPT-3 as a general-purpose data annotator in NLP.

对于自然语言处理中的许多任务，将知识从一个域转移到另一个领域至关重要，尤其是当目标域中的可用数据量受到限制时。在这项工作中，我们在指定实体识别（NER）的背景下提出了一种新颖的域适应方法。我们提出了一种两步方法，该方法由可变基本模块和模板模块组成，该模块在简单的描述模式的帮助下利用了预训练的语言模型中捕获的知识。我们的方法简单而通用，可以在几次射击和零拍设置中应用。评估我们在许多不同数据集中的轻量级方法表明，它可以将最新基准的性能提高2-5％的F1分数。

GPT-3等大型自回归语言模型是几秒钟的学习者，可以在没有微调的情况下执行各种语言任务。虽然已知这些模型能够共同代表许多不同的语言，但他们的培训数据由英语主导，可能限制了它们的交叉概括。在这项工作中，我们在覆盖多种语言的平衡语料库上培训多语言自回归语言模型，并在广泛的任务中研究他们几乎没有零点的学习能力。我们最大的模型，具有75亿参数，在20多种代表语言中，在几种代表语言中，在几种代表性语言中，在几种代表性语言中，在多语言型号推理中表现出可比大小的GPT-3（在0次设置和0次拍摄设置中的绝对精度改善+ 7.4％ 4-拍摄设置中的9.4％）和自然语言推理（每次拍摄和4次设置中的每一个+ 5.4％）。在Flores-101机器翻译基准测试中，我们的模型优于GPT-3在182个翻译方向上有32个培训例子，同时超过45个方向的官方监督基线。我们介绍了模型成功和失败的位置的详细分析，特别是它尤其显示在某些任务中实现交叉语境的内容学习，而仍然存在改善表面的鲁棒性和适应没有a的任务的余地自然冻结形式。最后，我们评估我们在仇恨语音检测中以五种语言的仇恨语音检测的模型，并发现它具有与可比大小的GPT-3模型类似的限制。

本文探讨了提高语言模型的零次学习能力的简单方法。我们表明，指令调整 - 通过对说明书中所述的任务集合微调语言模型 - 大幅提升零射门上看不见任务中的表现。我们采取预训练的语言模型和指令调整它通过自然语言指令模板语言表达了60NLP任务137B参数。我们评估这种指令调整模型，我们称之为FLAN，在看不见的任务类型。FLAN显着改善其未修饰的对应的性能和超过25的20个任务，我们评估零射门175BGPT-3。FLAN甚至GPT-3通过在安利，RTE，BoolQ，AI2-ARC，OpenbookQA和StoryCloze大比分胜过几拍。消融研究显示任务和模型的规模，这个数字是指令调整取得成功的关键组成部分。

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.

动机：生物医学研究人员和临床从业者的常年挑战是随着出版物和医疗票据的快速增长而待的。自然语言处理（NLP）已成为驯服信息超载的有希望的方向。特别是，大型神经语言模型通过预先绘制的文本预测，通过各种NLP应用中的BERT模型的成功示例，便于通过预先绘制的预先来进行学习。然而，用于结束任务的微调此类模型仍然具有挑战性，特别是具有小标记数据集，这些数据集是生物医学NLP的常见。结果：我们对生物医学NLP的微调稳定性进行了系统研究。我们表明FineTuning性能可能对预先预订的设置敏感，尤其是在低资源域中。大型型号有可能获得更好的性能，但越来越多的模型大小也加剧了FineTuning不稳定性。因此，我们对解决微调不稳定的技术进行了全面的探索。我们表明，这些技术可以大大提高低源生物医学NLP应用的微调性能。具体地，冻结下层有助于标准伯特基型号，而完整的衰减对于BERT-LARD和Electra型号更有效。对于低资源文本相似性任务，如生物，重新初始化顶层是最佳策略。总体而言，占星型词汇和预制促进更强大的微调模型。基于这些调查结果，我们在广泛的生物医学NLP应用方面建立了新的技术。可用性和实施​​：为了促进生物医学NLP的进展，我们释放了我们最先进的预订和微调模型：https://aka.ms/blurb。

Pre-trained Language Models (PLMs) have been applied in NLP tasks and achieve promising results. Nevertheless, the fine-tuning procedure needs labeled data of the target domain, making it difficult to learn in low-resource and non-trivial labeled scenarios. To address these challenges, we propose Prompt-based Text Entailment (PTE) for low-resource named entity recognition, which better leverages knowledge in the PLMs. We first reformulate named entity recognition as the text entailment task. The original sentence with entity type-specific prompts is fed into PLMs to get entailment scores for each candidate. The entity type with the top score is then selected as final label. Then, we inject tagging labels into prompts and treat words as basic units instead of n-gram spans to reduce time complexity in generating candidates by n-grams enumeration. Experimental results demonstrate that the proposed method PTE achieves competitive performance on the CoNLL03 dataset, and better than fine-tuned counterparts on the MIT Movie and Few-NERD dataset in low-resource settings.

我们研究了很少的细粒实体键入（FET）的问题，其中只有几个带注释的实体对每种实体类型提供了上下文。最近，基于及时的调整通过将实体类型分类任务作为“填补空白”的问题来表明在几次射击方案中表现出优越的性能。这允许有效利用预训练的语言模型（PLM）的强语建模能力。尽管当前基于及时的调整方法成功了，但仍有两个主要挑战：（1）提示中的口头化器要么是由外部知识基础手动设计或构建的，而无需考虑目标语料库和标签层次结构信息，而且（2）当前方法主要利用PLM的表示能力，但没有通过广泛的通用域预训练来探索其产生的功率。在这项工作中，我们为由两个模块组成的几个弹药fet提出了一个新颖的框架：（1）实体类型标签解释模块自动学习将类型标签与词汇联系起来，通过共同利用几个播放实例和标签层次结构和标签层次结构，以及（2）基于类型的上下文化实例生成器根据给定实例生成新实例，以扩大培训集以更好地概括。在三个基准数据集上，我们的模型优于大量利润的现有方法。可以在https://github.com/teapot123/fine-graining-entity-typing上找到代码。

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.

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.