Existing language models (LMs) predict tokens with a softmax over a finite vocabulary, which can make it difficult to predict rare tokens or phrases. We introduce NPM, the first nonparametric masked language model that replaces this softmax with a nonparametric distribution over every phrase in a reference corpus. We show that NPM can be efficiently trained with a contrastive objective and an in-batch approximation to full corpus retrieval. Zero-shot evaluation on 9 closed-set tasks and 7 open-set tasks demonstrates that NPM outperforms significantly larger parametric models, either with or without a retrieve-and-generate approach. It is particularly better on dealing with rare patterns (word senses or facts), and predicting rare or nearly unseen words (e.g., non-Latin script). We release the model and code at github.com/facebookresearch/NPM.
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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。
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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。
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We introduce TeSS (Text Similarity Comparison using Sentence Encoder), a framework for zero-shot classification where the assigned label is determined by the embedding similarity between the input text and each candidate label prompt. We leverage representations from sentence encoders optimized to locate semantically similar samples closer to each other in embedding space during pre-training. The label prompt embeddings serve as prototypes of their corresponding class clusters. Furthermore, to compensate for the potentially poorly descriptive labels in their original format, we retrieve semantically similar sentences from external corpora and additionally use them with the original label prompt (TeSS-R). TeSS outperforms strong baselines on various closed-set and open-set classification datasets under zero-shot setting, with further gains when combined with label prompt diversification through retrieval. These results are robustly attained to verbalizer variations, an ancillary benefit of using a bi-encoder. Altogether, our method serves as a reliable baseline for zero-shot classification and a simple interface to assess the quality of sentence encoders.
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We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. Span-BERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERT large , our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE. 1 * Equal contribution. 1 Our code and pre-trained models are available at https://github.com/facebookresearch/ SpanBERT.
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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。
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多语言语言模型（\ mllms），如mbert，xlm，xlm-r，\ textit {etc。}已成为一种可行的选择，使预先估计到大量语言的力量。鉴于他们的成功在零射击转移学习中，在（i）建立更大的\ mllms〜覆盖了大量语言（ii）创建覆盖更广泛的任务和语言来评估的详尽工作基准mllms〜（iii）分析单音零点，零拍摄交叉和双语任务（iv）对Monolingual的性能，了解\ mllms〜（v）增强（通常）学习的通用语言模式（如果有的话）有限的容量\ mllms〜以提高他们在已见甚至看不见语言的表现。在这项调查中，我们审查了现有的文学，涵盖了上述与\ MLLMS有关的广泛研究领域。根据我们的调查，我们建议您有一些未来的研究方向。
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对于开放式域问题的密集检索已被证明通过在问题通道对的大型数据集上培训来实现令人印象深刻的性能。我们调查是否可以以自我监督的方式学习密集的检索，并有效地应用没有任何注释。我们观察到这种情况下的检索斗争的现有借用模型，并提出了一种设计用于检索的新预制方案：重复跨度检索。我们在文档中使用经常性跨度来创建用于对比学习的伪示例。由此产生的模型 - 蜘蛛 - 在广泛的ODQA数据集上没有任何示例，并且与BM25具有竞争力，具有强烈的稀疏基线。此外，蜘蛛通常优于DPR在其他数据集的问题上培训的DPR培训的强大基线。我们将蜘蛛与BM25结合的混合猎犬改进了所有数据集的组件，并且通常与域中DPR模型具有竞争力，这些模型培训数万例培训。
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语言模型是通过有限的输入集定义的，当我们尝试扩展支持语言的数量时，该输入会产生词汇瓶颈。解决此瓶颈会导致在嵌入矩阵中可以表示的与输出层中的计算问题之间的权衡。本文介绍了基于像素的语言编码器Pixel，这两个问题都没有遭受这些问题的影响。 Pixel是一种验证的语言模型，可将文本作为图像呈现，使基于拼字法相似性或像素的共激活的语言传输表示形式。 Pixel经过训练可以重建蒙版贴片的像素，而不是预测令牌上的分布。我们在与BERT相同的英语数据上为8600万参数像素模型预告，并对包括各种非拉丁语脚本在内的类型上多样化的语言中的句法和语义任务进行了评估。我们发现，Pixel在预读取数据中找不到的脚本上的句法和语义处理任务大大优于BERT，但是在使用拉丁文脚本时，Pixel比BERT稍弱。此外，我们发现像素对嘈杂的文本输入比bert更强大，进一步证实了用像素建模语言的好处。
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信息检索是自然语言处理中的重要组成部分，用于知识密集型任务，如问题应答和事实检查。最近，信息检索已经看到基于神经网络的密集检索器的出现，作为基于术语频率的典型稀疏方法的替代方案。这些模型在数据集和任务中获得了最先进的结果，其中提供了大型训练集。但是，它们不会很好地转移到没有培训数据的新域或应用程序，并且通常因未经监督的术语 - 频率方法（例如BM25）的术语频率方法而言。因此，自然问题是如果没有监督，是否有可能训练密集的索取。在这项工作中，我们探讨了对比学习的限制，作为培训无人监督的密集检索的一种方式，并表明它导致强烈的检索性能。更确切地说，我们在15个数据集中出现了我们的模型胜过BM25的Beir基准测试。此外，当有几千例的示例可用时，我们显示微调我们的模型，与BM25相比，这些模型导致强大的改进。最后，当在MS-Marco数据集上微调之前用作预训练时，我们的技术在Beir基准上获得最先进的结果。
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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。
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Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dualencoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system greatly by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks. 1 * Equal contribution 1 The code and trained models have been released at https://github.com/facebookresearch/DPR.
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Transfer learning, where a model is first pre-trained on a data-rich task before being finetuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.
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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.
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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。
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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.
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Although large language models can be prompted for both zero- and few-shot learning, performance drops significantly when no demonstrations are available. In this paper, we introduce Z-ICL, a new zero-shot method that closes the gap by constructing pseudo-demonstrations for a given test input using a raw text corpus. Concretely, pseudo-demonstrations are constructed by (1) finding the nearest neighbors to the test input from the corpus and pairing them with random task labels, and (2) applying a set of techniques to reduce the amount of direct copying the model does from the resulting demonstrations. Evaluation on nine classification datasets shows that Z-ICL outperforms previous zero-shot methods by a significant margin, and is on par with in-context learning with labeled training data in the few-shot setting. Overall, Z-ICL provides a significantly higher estimate of the zero-shot performance levels of a model, and supports future efforts to develop better pseudo-demonstrations that further improve zero-shot results.
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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。
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为英语以外的其他语言构建有效的开放式问题答案（开放质量质量质量）系统可能是具有挑战性的，这主要是由于缺乏标记的培训数据。我们提出了一种数据有效的方法来引导此类系统，以使用英语以外的其他语言。我们的方法只需要在给定语言中，以及机器翻译的数据以及至少一个双语语言模型中有限的质量检查资源。为了评估我们的方法，我们为冰岛语言构建了这样的系统，并评估了Trivia风格数据集的性能。用于培训的语料库是英语的，但机器被翻译成冰岛。我们训练双语的冰岛/英语模型，以嵌入英语背景和冰岛问题，并在用密集酶引入的方法之后（Lee等，2021）。最终的系统是冰岛和英语之间的开放式域杂志系统。最后，该系统适用于仅冰岛开放式质量检查，以说明如何有效地创建开放的QA系统，而使用感兴趣的语言对策划数据集的访问有限。
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Multilingual Pretrained Language Models (MPLMs) have shown their strong multilinguality in recent empirical cross-lingual transfer studies. In this paper, we propose the Prompts Augmented by Retrieval Crosslingually (PARC) pipeline to improve the zero-shot performance on low-resource languages (LRLs) by augmenting the context with semantically similar sentences retrieved from a high-resource language (HRL) as prompts. PARC improves the zero-shot performance on three downstream tasks (binary sentiment classification, topic categorization and natural language inference) with multilingual parallel test sets across 10 LRLs covering 6 language families in both unlabeled settings (+5.1%) and labeled settings (+16.3%). PARC-labeled also outperforms the finetuning baseline by 3.7%. We find a significant positive correlation between cross-lingual transfer performance on one side, and the similarity between the high- and low-resource languages as well as the amount of low-resource pretraining data on the other side. A robustness analysis suggests that PARC has the potential to achieve even stronger performance with more powerful MPLMs.
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