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。

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.

大型语言模型在各种任务上显示出令人印象深刻的几次结果。但是，当知识是此类结果的关键时，就像问题回答和事实检查之类的任务一样，似乎需要存储知识的大量参数计数。众所周知，检索增强模型可以在不需要多个参数的情况下在知识密集的任务上表现出色，但是目前尚不清楚它们是否在几个弹药设置中工作。在这项工作中，我们介绍了地图集，这是一个经过精心设计和预先训练的增强语言模型，能够通过很少的培训示例学习知识密集型任务。我们对包括MMLU，苏格兰短裙和归类等各种任务进行评估，并研究文档索引内容的影响，表明它可以很容易地进行更新。值得注意的是，在自然问题上仅使用64个示例在自然问题上达到超过42 \％的准确性，尽管参数少了50倍，但比540B参数模型的表现优于540b参数模型。

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.

本文着重于几次NLP任务的文本数据增强。现有的数据增强算法要么使用一个小型培训集来生成新的合成数据，要么利用与任务无关的启发式规则（例如，同义词替代）或微调通用预训练的语言模型（例如GPT2）。因此，这些方法具有特定于任务的知识，并且仅限于在简单任务中为弱基线产生低质量的合成数据。为了解决这个问题，我们提出了知识混合数据增强模型（KNOWDA）：使用知识混合培训（KOMT）在不同的NLP任务的混合物上预测的编码器LM。 KOMT是一种培训程序，将各种异质NLP任务的输入示例重新定义为统一的文本到文本格式，并采用不同粒度的目标，以学习生成部分或完整的样本。在KOMT的帮助下，Knowda可以隐含地将所需的特定于任务的知识从任务的混合中隐含地结合在一起，并通过一些给定的实例迅速掌握目标任务的固有综合定律。据我们所知，我们是首次尝试将任务数量扩展到多任务共同培训以进行数据扩展。广泛的实验表明，i）Knowda成功地通过少量基准的基准成功地提高了Albert和Deberta的表现，表现优于先前的最新数据增强基线； ii）KNOWDA还可以改善少数弹药任务的模型性能，这是KOMT中未包含的固定任务类型。

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。

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。

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。

培训和评估语言模型越来越多地要求构建元数据 - 多样化的策划数据收集，并具有清晰的出处。自然语言提示最近通过将现有的，有监督的数据集转换为多种新颖的预处理任务，突出了元数据策划的好处，从而改善了零击的概括。尽管将这些以数据为中心的方法转化为生物医学语言建模的通用域文本成功，但由于标记的生物医学数据集在流行的数据中心中的代表性大大不足，因此仍然具有挑战性。为了应对这一挑战，我们介绍了BigBio一个由126个以上的生物医学NLP数据集的社区库，目前涵盖12个任务类别和10多种语言。 BigBio通过对数据集及其元数据进行程序化访问来促进可再现的元数据策划，并与当前的平台兼容，以及时工程和端到端的几个/零射击语言模型评估。我们讨论了我们的任务架构协调，数据审核，贡献指南的过程，并概述了两个说明性用例：生物医学提示和大规模，多任务学习的零射门评估。 BigBio是一项持续的社区努力，可在https://github.com/bigscience-workshop/biomedical上获得。

Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few-shot generalization to unseen tasks. However, there is a limited understanding of the performance trade-offs of different decisions made during the instruction-tuning process. These decisions include the scale and diversity of the instruction-tuning benchmark, different task sampling strategies, fine-tuning with and without demonstrations, training using specialized datasets for reasoning and dialogue, and finally, the fine-tuning objectives themselves. In this paper, we characterize the effect of instruction-tuning decisions on downstream task performance when scaling both model and benchmark sizes. To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks. Through the lens of this framework, we first present insights about instruction-tuning decisions as applied to OPT-30B and further exploit these insights to train OPT-IML 30B and 175B, which are instruction-tuned versions of OPT. OPT-IML demonstrates all three generalization abilities at both scales on four different evaluation benchmarks with diverse tasks and input formats -- PromptSource, FLAN, Super-NaturalInstructions, and UnifiedSKG. Not only does it significantly outperform OPT on all benchmarks but is also highly competitive with existing models fine-tuned on each specific benchmark. We release OPT-IML at both scales, together with the OPT-IML Bench evaluation framework.

In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark, introduced a little over one year ago, offers a single-number metric that summarizes progress on a diverse set of such tasks, but performance on the benchmark has recently surpassed the level of non-expert humans, suggesting limited headroom for further research. In this paper we present SuperGLUE, a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard. SuperGLUE is available at super.gluebenchmark.com.

很少拍摄的NLP研究非常活跃，但在不相交的研究线程中进行了评估套件，缺乏挑战性熟练的测试设置，并且无法采用仔细的实验​​设计。因此，社区不知道哪种技术最佳，甚至它们优于简单的基线。在回应中，我们制定了Flex原理，这一要求和统一，严谨，有效和成本敏感的少量NLP评估的一系列要求和最佳实践。这些原则包括样本量设计，采用基准设计的新方法，以优化统计准确性和精度，同时保持评估成本可管理。在原则之后，我们释放了Flex基准，其中包括四个几次传输设置，零拍摄评估和涵盖不同NLP任务的公共排行榜。此外，我们统一，一个基于提示的模型，用于几次学习，统一借鉴和FineTuning迅速格式，避免了最近基于及时的基于及时的方法的复杂机器，以便调整下游任务格式到语言模型预介质目标。我们证明，尽管很简单，植物实现了与流行的元学习和基于及时的方法竞争的结果。

在这项工作中，我们证明了多种语的大规模序列到序列（SEQ2SEQ）模型，该模型是通过Denoising和因果语言建模（CLM）任务的混合物进行训练的，比仅解码器模型更有效地进行了效率的学习者在各种任务上。特别是，我们培训了一个名为Alexa教师模型（Alexatm 20b）的200亿个参数多语言SEQ2SEQ模型，并表明它在1-Shot摘要任务上实现了最先进的（SOTA）性能，超过了更大的540B PALM DOPODER模型。 Alexatm 20b还可以在1-Shot Machine翻译中实现SOTA，尤其是对于低资源语言，几乎所有语言对（阿拉伯语，英语，法语，德语，德语，印地语，意大利语，日语，以及flores-101数据集上的泰卢固语）。我们还显示了零拍设置，AlexATM 20B在SuperGlue和SqueadV2数据集上的表现优于GPT3（175B），并在XNLI，XCOPA，PAWS-X和XWINOGRAD等多语言任务上提供SOTA性能。总体而言，我们的结果为SEQ2SEQ模型提供了一个令人信服的案例，作为大型语言模型（LLM）培训的仅解码器模型的强大替代方法。

We demonstrate that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even becoming competitive with prior state-ofthe-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous nonsparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks. We also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora.

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.

大型语言模型在零拍摄设置中的许多自然语言处理（NLP）任务中表现出令人印象深刻的性能。我们询问这些模型是否展示了致辞语言 - NLP应用的关键组成部分 - 通过评估四个偶数基准的模型。我们发现大型语言模型的令人印象深刻的零射击性能主要是由于我们的基准测试中的数据集偏差。我们还表明，零拍摄性能对基准的超参数和相似性敏感到预训练数据集。此外，当在几次拍摄设置中评估模型时，我们没有观察大量改进。最后，与以前的工作相比，我们发现利用明确的致辞知识并没有产生重大改善。

Triplet extraction aims to extract entities and their corresponding relations in unstructured text. Most existing methods train an extraction model on high-quality training data, and hence are incapable of extracting relations that were not observed during training. Generalizing the model to unseen relations typically requires fine-tuning on synthetic training data which is often noisy and unreliable. In this paper, we argue that reducing triplet extraction to a template filling task over a pre-trained language model can equip the model with zero-shot learning capabilities and enable it to leverage the implicit knowledge in the language model. Embodying these ideas, we propose a novel framework, ZETT (ZEro-shot Triplet extraction by Template infilling), that is based on end-to-end generative transformers. Our experiments show that without any data augmentation or pipeline systems, ZETT can outperform previous state-of-the-art models with 25% less parameters. We further show that ZETT is more robust in detecting entities and can be incorporated with automatically generated templates for relations.

基础模型由于在广泛的下游应用中的有效性而受到了很多关注。尽管在体系结构方面存在很大的融合，但大多数审慎的模型通常仍用于特定任务或模式。在这项工作中，我们建议将语言模型用作各种基础模型的通用接口。一系列预处理的编码者感知到了多种方式（例如视觉和语言），并与扮演通用任务层角色的语言模型对接。我们提出了一个半伴侣的语言建模目标，以共同确定界面和模块化编码器。我们从因果关系和非因果建模中涵盖了优势和能力，从而结合了两个世界的最佳状态。具体而言，所提出的方法不仅从因果语言建模中继承了内在学习和开放式生成的能力，而且由于双向编码器而有利于填补。更重要的是，我们的方法无缝地解锁了上述功能的组合，例如，通过填充编码器启用了文本学习或指导。各种仅语言和视觉语言基准的实验结果表明，我们的模型表现优于或与鉴定，零弹性概括和几乎没有的学习的专业模型竞争。

In this work, we explore "prompt tuning," a simple yet effective mechanism for learning "soft prompts" to condition frozen language models to perform specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signals from any number of labeled examples. Our end-to-end learned approach outperforms GPT-3's few-shot learning by a large margin. More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method "closes the gap" and matches the strong performance of model tuning (where all model weights are tuned). This finding is especially relevant because large models are costly to share and serve and the ability to reuse one frozen model for multiple downstream tasks can ease this burden. Our method can be seen as a simplification of the recently proposed "prefix tuning" of Li and Liang (2021) and we provide a comparison to this and other similar approaches. Finally, we show that conditioning a frozen model with soft prompts confers benefits in robustness to domain transfer and enables efficient "prompt ensembling." * Work done as a Google AI Resident.

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a;Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications.BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).