激活功能可以对降低输入数据的拓扑复杂性产生重大影响，从而提高模型的性能。选择合适的激活函数是神经模型设计中的重要步骤。但是，在基于变压器的语言模型中很少讨论或探索激活功能的选择。事先选择它们的激活功能，然后从预训练中固定到微调。结果，在这个漫长的生命周期中，无法调整它们对模型的电感偏见。此外，随后开发的模型（例如Roberta，Bart和GPT-3）经常跟进先前的工作（例如BERT），以使用相同的激活函数而无需合理。在本文中，我们研究了变压器体系结构中使用理性激活函数（RAF）（RAF）的有效性。与常规，预定义的激活功能相反，RAF可以根据输入数据自适应地学习最佳激活功能。我们的实验表明，基于RAF的变压器（RAFT）比具有GELU函数的香草BERT的验证性更低。我们进一步评估了低和全数据设置中下游任务的筏。我们的结果表明，筏在大多数任务和设置上都优于对应模型。例如，在低数据表情况下（有100个训练示例），木筏在胶水基准上的表现平均高出5.71点，在全数据设置的小队中，平均得分为2.05分。对学到的RAF的形状的分析进一步揭示了它们在预训练模型的不同层之间有很大的变化，并且看起来与常规激活函数大多不同。 RAFT为根据学习的激活功能打开了一个新的研究方向，用于分析和解释预训练的模型。

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).

Fine-tuning large pre-trained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new model is required for every task. As an alternative, we propose transfer with adapter modules. Adapter modules yield a compact and extensible model; they add only a few trainable parameters per task, and new tasks can be added without revisiting previous ones. The parameters of the original network remain fixed, yielding a high degree of parameter sharing. To demonstrate adapter's effectiveness, we transfer the recently proposed BERT Transformer model to 26 diverse text classification tasks, including the GLUE benchmark. Adapters attain near state-of-the-art performance, whilst adding only a few parameters per task. On GLUE, we attain within 0.4% of the performance of full fine-tuning, adding only 3.6% parameters per task. By contrast, fine-tuning trains 100% of the parameters per task.

最近的参数效率语言模型调整（PELT）方法可以使微调的性能与较少的可训练参数相匹配，并且在训练数据受到限制时尤其表现良好。但是，不同的PELT方法在相同的任务上的性能可能会有所不同，因此为特定任务选择最合适的方法是不平凡的，尤其是考虑到快速增长的新PELT方法和任务。鉴于模型多样性和模型选择的难度，我们提出了一个统一的框架Unipelt，该框架将不同的毛皮方法纳入了子模型，并学会了激活最适合当前数据或通过门控机制设置的方法。在胶水基准上，与最佳的单个毛皮方法相比，UniPelt始终达到1〜4％的增长，而其融合甚至超过了不同设置下的微调。此外，UniPelt通常超过上限，该上限在每个任务上单独使用的所有子模型的最佳性能，表明多种PELT方法的混合物可能本质上比单个方法更有效。

我们介绍了BitFit，这是一种稀疏的重点方法，其中仅修改了模型的偏差（或其中一个子集）。我们表明，通过在预训练的BERT模型上应用BITFIT的小型至中等训练数据具有竞争力（有时比）对整个模型进行微调。对于较大的数据，该方法与其他稀疏微调方法具有竞争力。除了它们的实际实用性外，这些发现与理解常用的填补过程的问题有关：它们支持以下假设：填充主要是关于揭示通过语言模型培训引起的知识，而不是学习新的任务特定的语言知识。

Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Encoder Representations from Transformers) has achieved amazing results in many language understanding tasks. In this paper, we conduct exhaustive experiments to investigate different fine-tuning methods of BERT on text classification task and provide a general solution for BERT fine-tuning. Finally, the proposed solution obtains new state-of-the-art results on eight widely-studied text classification datasets. 1

在培训数据中拟合复杂的模式，例如推理和争议，是语言预训练的关键挑战。根据最近的研究和我们的经验观察，一种可能的原因是训练数据中的一些易于适应的模式，例如经常共同发生的单词组合，主导和伤害预训练，使模型很难适合更复杂的信息。我们争辩说，错误预测可以帮助找到危害语言理解的这种主导模式。当发生错误预测时，应该经常与导致MIS预测的模型拟合的MIS预测字相同的模式。如果我们可以添加正规化以培训模型，当MIS预测发生并更多地对待更微妙的模式时，可以在更多信息上缩小到这种主导模式时，可以在预训练中有效地安装更多信息。在此动机之后，我们提出了一种新的语言预培训方法，错误预测作为伤害警报（MPA）。在MPA中，当在预训练期间发生错误预测时，我们使用其共同发生信息来指导自我关注模块的多个头部。变压器模块中的一些自我关注头经过优化，以将更低的注意重量分配给频繁地在误报中的输入句子中的单词，同时将更高权重分配给另一个单词。通过这样做，变压器模型训练，以依赖于主导的频繁共同发生模式，而在误报中，当发生错误预测时，在剩余更复杂的信息上更加关注更多。我们的实验表明，MPA加快了伯特和电器的预训练，并提高了他们对下游任务的表现。

Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions, respectively. Second, an enhanced mask decoder is used to incorporate absolute positions in the decoding layer to predict the masked tokens in model pre-training. In addition, a new virtual adversarial training method is used for fine-tuning to improve models' generalization. We show that these techniques significantly improve the efficiency of model pre-training and the performance of both natural language understand (NLU) and natural langauge generation (NLG) downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). Notably, we scale up DeBERTa by training a larger version that consists of 48 Transform layers with 1.5 billion parameters. The significant performance boost makes the single DeBERTa model surpass the human performance on the SuperGLUE benchmark (Wang et al., 2019a) for the first time in terms of macro-average score (89.9 versus 89.8), and the ensemble DeBERTa model sits atop the SuperGLUE leaderboard as of January 6, 2021, outperforming the human baseline by a decent margin (90.3 versus 89.8). The pre-trained DeBERTa models and the source code were released at: https://github.com/microsoft/DeBERTa 1 .

Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations and longer training times. To address these problems, we present two parameterreduction techniques to lower memory consumption and increase the training speed of BERT (Devlin et al., 2019). Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT. We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and SQuAD benchmarks while having fewer parameters compared to BERT-large. The code and the pretrained models are available at https://github.com/google-research/ALBERT. * Work done as an intern at Google Research, driving data processing and downstream task evaluations.

The pre-training of masked language models (MLMs) consumes massive computation to achieve good results on downstream NLP tasks, resulting in a large carbon footprint. In the vanilla MLM, the virtual tokens, [MASK]s, act as placeholders and gather the contextualized information from unmasked tokens to restore the corrupted information. It raises the question of whether we can append [MASK]s at a later layer, to reduce the sequence length for earlier layers and make the pre-training more efficient. We show: (1) [MASK]s can indeed be appended at a later layer, being disentangled from the word embedding; (2) The gathering of contextualized information from unmasked tokens can be conducted with a few layers. By further increasing the masking rate from 15% to 50%, we can pre-train RoBERTa-base and RoBERTa-large from scratch with only 78% and 68% of the original computational budget without any degradation on the GLUE benchmark. When pre-training with the original budget, our method outperforms RoBERTa for 6 out of 8 GLUE tasks, on average by 0.4%.

Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens. While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a more sample-efficient pre-training task called replaced token detection. Instead of masking the input, our approach corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not. Thorough experiments demonstrate this new pre-training task is more efficient than MLM because the task is defined over all input tokens rather than just the small subset that was masked out. As a result, the contextual representations learned by our approach substantially outperform the ones learned by BERT given the same model size, data, and compute. The gains are particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained using 30x more compute) on the GLUE natural language understanding benchmark. Our approach also works well at scale, where it performs comparably to RoBERTa and XLNet while using less than 1/4 of their compute and outperforms them when using the same amount of compute.

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。

变压器注意机制的二次计算和内存复杂性限制了对长序列建模的可扩展性。在本文中，我们提出了Luna，一种线性统一嵌套关注机制，使Softmax注意力具有两个嵌套线性关注功能，仅产生线性（与二次）的时间和空间复杂度相反。具体地，通过第一注意功能，LUNA将输入序列包装成固定长度的序列。然后，使用第二关注功能未包装包装序列。与更传统的关注机制相比，LUNA引入具有固定长度的附加序列作为输入和额外的相应输出，允许LUNA线性地进行关注操作，同时还存储足够的上下文信息。我们对三个序列建模任务的基准进行了广泛的评估：长上下文序列建模，神经机平移和大型预磨损的屏蔽语言建模。竞争甚至更好的实验结果表明了Luna的有效性和效率与各种各样相比

专家层（MOES）的混合物通过条件计算实现语言模型的高效缩放。本文提出了一个详细的实证研究，自回归鞋语言模型与广泛的设置中的密集模型相比：在域外语言建模，零和少量射击和全部微调。除了微调外，我们发现Moes基本上更加计算效率。在更适度的培训预算下，MOES可以使用$ \ SIM值4倍的计算，符合密集模型的性能。该差距在比例下变窄，但我们最大的MOE模型（1.1T参数）始终如一地优于计算等效的密集模型（6.7b参数）。总体而言，这种表现差距在任务和域中有很大差异，表明MOE和密集模型以不值得研究的方式概括不同的方式。我们使我们的代码和模型公开可用于研究使用。

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.

我们为大规模训练的大规模训练语言模型提供了更简单，更稀疏，更快的算法，这些算法在许多标准的NLP任务上实现了最新的隐私与实用性权衡。我们为此问题提出了一个元框架，这是受高度参数效率方法进行微调成功的启发。我们的实验表明，这些方法的差异化适应能力在三个重要方面优于以前的私人算法：实用程序，隐私以及私人培训的计算和记忆成本。在许多经常研究的数据集中，私人模型的实用性接近了非私人模型的方法。例如，在MNLI数据集上，我们使用Roberta-large的准确度为87.8 \％$，使用Roberta-Base $ 83.5 \％$，其隐私预算为$ \ Epsilon = 6.7 $。相比之下，缺乏隐私限制，罗伯塔·莱格（Roberta-Large）的准确度为$ 90.2 \％$。我们的发现对于自然语言生成任务类似。与DART，GPT-2-SMALL，GPT-2中，GPT-2-MEDIUM，GPT-2-LARGE和GPT-2-XL的私人微调达到38.5、42.0、43.1和43.8（$ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 43.8） epsilon = 6.8，\ delta = $ 1E-5），而非私人基线为$ 48.1 $。我们所有的实验都表明，较大的模型更适合私人微调：虽然众所周知，它们旨在非优先实现卓越的准确性，但我们发现当引入隐私时，它们也更好地保持其准确性。

Recent trends in language modeling have focused on increasing performance through scaling, and have resulted in an environment where training language models is out of reach for most researchers and practitioners. While most in the community are asking how to push the limits of extreme computation, we ask the opposite question: How far can we get with a single GPU in just one day? We investigate the downstream performance achievable with a transformer-based language model trained completely from scratch with masked language modeling for a single day on a single consumer GPU. Aside from re-analyzing nearly all components of the pretraining pipeline for this scenario and providing a modified pipeline with performance close to BERT, we investigate why scaling down is hard, and which modifications actually improve performance in this scenario. We provide evidence that even in this constrained setting, performance closely follows scaling laws observed in large-compute settings. Through the lens of scaling laws, we categorize a range of recent improvements to training and architecture and discuss their merit and practical applicability (or lack thereof) for the limited compute setting.

Neural models that do not rely on pre-training have excelled in the keyphrase generation task with large annotated datasets. Meanwhile, new approaches have incorporated pre-trained language models (PLMs) for their data efficiency. However, there lacks a systematic study of how the two types of approaches compare and how different design choices can affect the performance of PLM-based models. To fill in this knowledge gap and facilitate a more informed use of PLMs for keyphrase extraction and keyphrase generation, we present an in-depth empirical study. Formulating keyphrase extraction as sequence labeling and keyphrase generation as sequence-to-sequence generation, we perform extensive experiments in three domains. After showing that PLMs have competitive high-resource performance and state-of-the-art low-resource performance, we investigate important design choices including in-domain PLMs, PLMs with different pre-training objectives, using PLMs with a parameter budget, and different formulations for present keyphrases. Further results show that (1) in-domain BERT-like PLMs can be used to build strong and data-efficient keyphrase generation models; (2) with a fixed parameter budget, prioritizing model depth over width and allocating more layers in the encoder leads to better encoder-decoder models; and (3) introducing four in-domain PLMs, we achieve a competitive performance in the news domain and the state-of-the-art performance in the scientific domain.

Language models with the Transformers structure have shown great performance in natural language processing. However, there still poses problems when fine-tuning pre-trained language models on downstream tasks, such as over-fitting or representation collapse. In this work, we propose HyPe, a simple yet effective fine-tuning technique to alleviate such problems by perturbing hidden representations of Transformers layers. Unlike previous works that only add noise to inputs or parameters, we argue that the hidden representations of Transformers layers convey more diverse and meaningful language information. Therefore, making the Transformers layers more robust to hidden representation perturbations can further benefit the fine-tuning of PLMs en bloc. We conduct extensive experiments and analyses on GLUE and other natural language inference datasets. Results demonstrate that HyPe outperforms vanilla fine-tuning and enhances generalization of hidden representations from different layers. In addition, HyPe acquires negligible computational overheads, and is better than and compatible with previous state-of-the-art fine-tuning techniques.

来自变压器（BERT）的双向编码器表示显示了各种NLP任务的奇妙改进，并且已经提出了其连续的变体来进一步提高预先训练的语言模型的性能。在本文中，我们的目标是首先介绍中国伯特的全文掩蔽（WWM）策略，以及一系列中国预培训的语言模型。然后我们还提出了一种简单但有效的型号，称为Macbert，这在几种方面提高了罗伯塔。特别是，我们提出了一种称为MLM作为校正（MAC）的新掩蔽策略。为了展示这些模型的有效性，我们创建了一系列中国预先培训的语言模型，作为我们的基线，包括BERT，Roberta，Electra，RBT等。我们对十个中国NLP任务进行了广泛的实验，以评估创建的中国人托管语言模型以及提议的麦克白。实验结果表明，Macbert可以在许多NLP任务上实现最先进的表演，我们还通过几种可能有助于未来的研究的调查结果来消融细节。我们开源我们的预先培训的语言模型，以进一步促进我们的研究界。资源可用：https://github.com/ymcui/chinese-bert-wwm