Task agnostic generative pretraining (GPT) has recently proved promising for zero- and few-shot learning, gradually diverting attention from the expensive supervised learning paradigm. Although the community is accumulating knowledge as to capabilities of English-language autoregressive models such as GPT-3 adopting this generative approach, scholarship about these models remains acutely Anglocentric. Consequently, the community currently has serious gaps in its understanding of this class of models, their potential, and their societal impacts in diverse settings, linguistic traditions, and cultures. To alleviate this issue for Arabic, a collection of diverse languages and language varieties with more than $400$ million population, we introduce JASMINE, a suite of powerful Arabic autoregressive Transformer language models ranging in size between 300 million-13 billion parameters. We pretrain our new models with large amounts of diverse data (400GB of text) from different Arabic varieties and domains. We evaluate JASMINE extensively in both intrinsic and extrinsic settings, using a comprehensive benchmark for zero- and few-shot learning across a wide range of NLP tasks. We also carefully develop and release a novel benchmark for both automated and human evaluation of Arabic autoregressive models focused at investigating potential social biases, harms, and toxicity in these models. We aim to responsibly release our models with interested researchers, along with code for experimenting with them

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。

Due to their crucial role in all NLP, several benchmarks have been proposed to evaluate pretrained language models. In spite of these efforts, no public benchmark of diverse nature currently exists for evaluation of Arabic. This makes it challenging to measure progress for both Arabic and multilingual language models. This challenge is compounded by the fact that any benchmark targeting Arabic needs to take into account the fact that Arabic is not a single language but rather a collection of languages and varieties. In this work, we introduce ORCA, a publicly available benchmark for Arabic language understanding evaluation. ORCA is carefully constructed to cover diverse Arabic varieties and a wide range of challenging Arabic understanding tasks exploiting 60 different datasets across seven NLU task clusters. To measure current progress in Arabic NLU, we use ORCA to offer a comprehensive comparison between 18 multilingual and Arabic language models. We also provide a public leaderboard with a unified single-number evaluation metric (ORCA score) to facilitate future research.

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.

大型语言模型经常经过数十万个计算天的训练，已经显示出零和少数学习的显着功能。鉴于它们的计算成本，如果没有大量资本，这些模型很难复制。对于通过API可用的少数产品，没有访问完整的模型权重，因此很难学习。我们提供开放训练的预训练变压器（OPT），这是一套仅解码器预训练的变压器，范围从12500万到175b参数，我们旨在与感兴趣的研究人员完全和负责任地分享。我们表明，OPT-175B与GPT-3相当，而仅需要1/7碳足迹才能开发。我们还释放了日志，详细介绍了我们面临的基础架构挑战，以及用于尝试所有发布模型的代码。

In this work, we introduce IndicXTREME, a benchmark consisting of nine diverse tasks covering 18 languages from the Indic sub-continent belonging to four different families. Across languages and tasks, IndicXTREME contains a total of 103 evaluation sets, of which 51 are new contributions to the literature. To maintain high quality, we only use human annotators to curate or translate\footnote{for IndicXParaphrase, where an automatic translation system is used, a second human verification and correction step is done.} our datasets. To the best of our knowledge, this is the first effort toward creating a standard benchmark for Indic languages that aims to test the zero-shot capabilities of pretrained language models. We also release IndicCorp v2, an updated and much larger version of IndicCorp that contains 20.9 billion tokens in 24 languages. We pretrain IndicBERT v2 on IndicCorp v2 and evaluate it on IndicXTREME to show that it outperforms existing multilingual language models such as XLM-R and MuRIL.

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。

通过自我监督的学习预先训练的大型语言模型在各种各样的任务上表现出令人印象深刻的零击功能。在这项工作中，我们介绍了Welm：一种针对中文的精心读取的预训练的语言模型，能够无缝执行不同类型的任务，以零或几次演示。 Welm通过“阅读”涵盖广泛主题的精选高质量语料库来接受10b参数的培训。我们表明，韦尔姆拥有有关各种领域和语言的广泛知识。在18个单语（中文）任务中，WELM可以大大优于现有的预训练模型，尺寸相似，并匹配高达25倍大的模型的性能。韦尔姆还表现出强大的多种语言和代码转换理解的能力，优于预先对30种语言进行预培训的现有多语言模型。此外，我们收集了人工编写的提示，并通过多次培训进行了大量的中文和微调韦尔姆的监督数据集。最终的模型可以实现对看不见的任务类型的强烈概括，并在零射门学习中优于无监督的韦尔姆。最后，我们证明韦尔姆具有解释和校准自己的决策的基本技能，这可能是未来研究的有希望的方向。我们的模型可以从https://welm.weixin.qq.com/docs/api/应用。

大型语言模型会产生类似人类的文本，这些文本推动了越来越多的应用。但是，最近的文献以及越来越多的现实世界观察表明，这些模型可以产生有毒，有偏见，不真实或其他有害的语言。尽管正在进行评估语言模型危害的工作，但要远见卓识转换出可能出现的危害可能会引起严格的基准。为了促进这种翻译，我们概述了六种表征有害文本的方式，这些方法在设计新基准时值得明确考虑。然后，我们将这些特征用作镜头来识别现有基准中的趋势和差距。最后，我们将它们应用于视角API的案例研究，这是一种毒性分类器，被广泛用于HARS基准。我们的特征提供了一块桥梁，可以在远见和有效评估之间转化。

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。

Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation). For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PALM 540B by a large margin (+9.4% on average). Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints, which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.

Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers generated by the language model reach 55 F1 on the CoQA dataset -matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and increasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations.

The BLOOM model is a large open-source multilingual language model capable of zero-shot learning, but its pretraining was limited to 46 languages. To improve its zero-shot performance on unseen languages, it is desirable to adapt BLOOM, but previous works have only explored adapting small language models. In this work, we apply existing language adaptation strategies to BLOOM and benchmark its zero-shot prompting performance on eight new languages. We find language adaptation to be effective at improving zero-shot performance in new languages. Surprisingly, adapter-based finetuning is more effective than continued pretraining for large models. In addition, we discover that prompting performance is not significantly affected by language specifics, such as the writing system. It is primarily determined by the size of the language adaptation data. We also add new languages to BLOOMZ, which is a multitask finetuned version of BLOOM capable of following task instructions zero-shot. We find including a new language in the multitask fine-tuning mixture to be the most effective method to teach BLOOMZ a new language. We conclude that with sufficient training data language adaptation can generalize well to diverse languages. Our code is available at \url{https://github.com/bigscience-workshop/multilingual-modeling/}.

Multilingual language models (MLMs) acquire valuable, generalizable linguistic information during pretraining and have advanced the state of the art on task-specific finetuning. So far, only ~ 28 out of ~2,000 African languages are covered in existing language models. We ameliorate this limitation by developing SERENGETI, a set of massively multilingual language model that covers 517 African languages and language varieties. We evaluate our novel models on eight natural language understanding tasks across 20 datasets, comparing to four MLMs that each cover any number of African languages. SERENGETI outperforms other models on 11 datasets across the eights tasks and achieves 82.27 average F-1. We also perform error analysis on our models' performance and show the influence of mutual intelligibility when the models are applied under zero-shot settings. We will publicly release our models for research.

姿态检测的目标是确定以目标朝向目标的文本中表达的视点。这些观点或上下文通常以许多不同的语言表达，这取决于用户和平台，这可以是本地新闻插座，社交媒体平台，新闻论坛等。然而，姿态检测的大多数研究已经限于使用单一语言和几个有限的目标，在交叉舌姿态检测很少有效。此外，标记数据的非英语来源通常稀缺，并具有额外的挑战。最近，大型多语言语言模型在许多非英语任务上大大提高了性能，尤其是具有有限数量的示例。这突出了模型预培训的重要性及其从少数例子中学习的能力。在本文中，我们展示了对日期交叉姿态检测的最全面的研究：我们在6名语言系列中使用12种语言的12种不同的数据集进行实验，每个都有6个低资源评估设置。对于我们的实验，我们构建了模式开发培训，提出了添加一种新颖的标签编码器来简化言语程序。我们进一步提出了基于情绪的姿态数据进行预培训，这在与几个强的基线相比，在低拍摄环境中显示了大量的6％F1绝对的增长。

语言可以用作再现和执行有害刻板印象和偏差的手段，并被分析在许多研究中。在本文中，我们对自然语言处理中的性别偏见进行了304篇论文。我们分析了社会科学中性别及其类别的定义，并将其连接到NLP研究中性别偏见的正式定义。我们调查了在对性别偏见的研究中应用的Lexica和数据集，然后比较和对比方法来检测和减轻性别偏见。我们发现对性别偏见的研究遭受了四个核心限制。 1）大多数研究将性别视为忽视其流动性和连续性的二元变量。 2）大部分工作都在单机设置中进行英语或其他高资源语言进行。 3）尽管在NLP方法中对性别偏见进行了无数的论文，但我们发现大多数新开发的算法都没有测试他们的偏见模型，并无视他们的工作的伦理考虑。 4）最后，在这一研究线上发展的方法基本缺陷涵盖性别偏差的非常有限的定义，缺乏评估基线和管道。我们建议建议克服这些限制作为未来研究的指导。

We present NusaCrowd, a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have has brought together 137 datasets and 117 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their effectiveness has been demonstrated in multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and its local languages. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and its local languages. Our work is intended to help advance natural language processing research in under-represented languages.

Language models can be prompted to perform a wide variety of zero- and few-shot learning problems. However, performance varies significantly with the choice of prompt, and we do not yet understand why this happens or how to pick the best prompts. In this work, we analyze the factors that contribute to this variance and establish a new empirical hypothesis: the performance of a prompt is coupled with the extent to which the model is familiar with the language it contains. Over a wide range of tasks, we show that the lower the perplexity of the prompt is, the better the prompt is able to perform the task. As a result, we devise a method for creating prompts: (1) automatically extend a small seed set of manually written prompts by paraphrasing using GPT3 and backtranslation and (2) choose the lowest perplexity prompts to get significant gains in performance.

最近已被证明大型语言模型在各种任务集中获得合理的零射普通化（Brown等，2020）。它已经假设这是语言模型的隐式多任务学习的结果，在语言模型中的预押（Radford等，2019）。可以通过明确的多任务学习直接引起零拍常规化？为了以缩放测试这个问题，我们开发一个系统，以便轻松地将任何自然语言任务映射到人类可读的提示表单中。我们转换一组大量的监督数据集，每个数据集都有多个提示，具有不同的措辞。这些提示的数据集允许基准测试模型执行完全看不见的任务的能力。我们介绍了一个普拉克尔编码器 - 解码器模型（Raffel等，2020; Lester等，2021），覆盖各种任务。该模型在多个标准数据集中达到强大的零点性能，通常优于其尺寸的型号超过16倍。此外，我们的方法对来自Big-替补基准测试的任务子集具有强烈性能，优于其尺寸的6倍。所有提示和培训的型号都可以在https://github.com/ bigscience-workshop / protectsource / httpsource / https：//huggingface.co/bigscience/t0pp。

GPT-3显示了培训的大规模语言模型（LMS）的卓越情调学习能力，培训数十亿规模数据。在这里，我们解决了GPT-3纸张报告的一些剩余问题，例如非英语LM，不同大小模型的性能，以及最近引入的迅速优化对上下文学习的效果。为实现这一目标，我们介绍了HyperClova，一个韩国VPT-3的韩国变体训练在一个以韩国为中心的560b标准的令牌。通过我们的韩国特定标记化，HyperClova与我们的培训配置增强，显示了韩国各种下游任务的最先进的上下游零射击和几秒钟学习表演。此外，我们展示了基于及时的学习的性能优势，并演示如何集成到迅速的工程管道中。然后，我们讨论了通过引入Hyperclova Studio，互动提示工程界面向ML的非专家提供AI原型设计能力来实现No Code AI范例的可能性。最后，我们展示了我们具有三个成功的内部应用程序的方法的潜力。