许多NLP任务需要处理超出预磨模模型的长度限制的长语境。为了将这些模型扩展到更长的文本序列，已经提出了许多有效的远程注意力变体。尽管沿着这个方向进行了丰富的研究，但仍然难以在实际用例中衡量这些模型的相对有效性，例如，如果我们在预先rain-yfetune范式之后应用这些模型。在这项工作中，我们的目标是对这些具有大规模和受控实验的这些新兴模型进行彻底的分析。对于每个关注变体，我们使用相同的长DOC语料库，然后使用相同的长DOC语料库，然后为现实世界的长情节任务进行芬特这些模型。我们的调查结果揭示了现有广泛使用的远程基准的陷阱，并显示任何经过测试的高效关注可以在标准预介质范式下击败一个简单的本地窗口关注。对本地注意力变化的进一步分析表明，即使是常用的注意力窗口重叠也没有必要实现良好的下游结果 - 使用不相交的本地关注，我们能够构建符合性能的更简单且更高效的Long-Doc QA模型霍尔福勒〜\ citep {longformer}其预先花费的一半。

我们提出了一项实证研究，以适应现有的经过验证的文本对文本模型，以备长期输入。通过沿预训练管道的三个轴的全面研究 - 模型架构，优化目标和训练式语料库，我们提出了一种有效的食谱，以从现有的短篇小说模型中构建长篇小说模型。具体而言，我们用汇总仪的块关注替换了变压器中的全部注意力，并使用蒙版的跨度预测任务为模型预算，长度不同。就训练训练的语料库而言，我们发现，与使用通常在其域覆盖范围中通常受到限制的现有长文档语料库相比，使用大型开放域语料库的随机串联的短篇小说可以提高性能。通过这些发现，我们建立了一个长篇文本模型，该模型可以在长篇文本质量检查任务上实现竞争性能，并在五个长文本摘要数据集上建立新的最新技术，通常优于先前的方法，具有较大的模型大小。

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

Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. In the recent months, a wide spectrum of efficient, fast Transformers have been proposed to tackle this problem, more often than not claiming superior or comparable model quality to vanilla Transformer models. To this date, there is no well-established consensus on how to evaluate this class of models. Moreover, inconsistent benchmarking on a wide spectrum of tasks and datasets makes it difficult to assess relative model quality amongst many models. This paper proposes a systematic and unified benchmark, Long-Range Arena, specifically focused on evaluating model quality under long-context scenarios. Our benchmark is a suite of tasks consisting of sequences ranging from 1K to 16K tokens, encompassing a wide range of data types and modalities such as text, natural, synthetic images, and mathematical expressions requiring similarity, structural, and visual-spatial reasoning. We systematically evaluate ten well-established long-range Transformer models (Reformers, Linformers, Linear Transformers, Sinkhorn Transformers, Performers, Synthesizers, Sparse Transformers, and Longformers) on our newly proposed benchmark suite. Long-Range Arena paves the way towards better understanding this class of efficient Transformer models, facilitates more research in this direction, and presents new challenging tasks to tackle. Our benchmark code will be released at https://github.com/google-research/long-range-arena.

Recent work has improved language models (LMs) remarkably by equipping them with a non-parametric memory component. However, most existing approaches only introduce mem-ories at testing time or represent them using a separately trained encoder, resulting in suboptimal training of the language model. In this work, we present TRIME, a novel yet simple training approach designed for training LMs with memory augmentation. Our approach uses a training objective that directly takes in-batch examples as accessible memory. We also present new methods for memory construction and data batching, which are used for adapting to different sets of memories--local, long-term, and external memory--at testing time. We evaluate TRIME on multiple language modeling and machine translation benchmarks and show that it is able to achieve significant improvements across all the settings. Concretely, TRIME reduces the perplexity from 18.70 to 15.37 on WIKITEXT-103, by effectively leveraging a large memory set from the training corpus. Compared to standard LM training, TRIME adds negligible computational overhead and is compatible with different neural architectures, making it a versatile solution for training memory-augmented LMs.

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).

最近的工作表明，（1）增加输入长度或（2）增加模型大小可以提高基于变压器的神经模型的性能。在本文中，我们提出了一个名为Longt5的新模型，我们探讨了同时缩放输入长度和模型大小的效果。具体而言，我们综合了从长输入变压器（ETC）的关注思路，并采用了从摘要预训练（PEGASU）的预训练策略进入可扩展的T5架构。结果是我们称之为{\ EM瞬态全球}（TGLOBAL）的新关注机制，这些机制是模仿等本地/全球注意力机制，但不需要额外的侧面输入。我们能够实现最先进的结果，以若干摘要任务，优于问题应答任务的原始T5模型。

近年来，基于变压器的预训练模型已获得了很大的进步，成为自然语言处理中最重要的骨干之一。最近的工作表明，变压器内部的注意力机制可能不需要，卷积神经网络和基于多层感知器的模型也已被研究为变压器替代方案。在本文中，我们考虑了一个用于语言模型预训练的图形循环网络，该网络通过本地令牌级通信为每个序列构建一个图形结构，以及与其他代币解耦的句子级表示。原始模型在受监督培训下的特定领域特定文本分类中表现良好，但是，其通过自我监督的方式学习转移知识的潜力尚未得到充分利用。我们通过优化体系结构并验证其在更通用的语言理解任务（英语和中文）中的有效性来填补这一空白。至于模型效率，我们的模型在基于变压器的模型中而不是二次复杂性，而是具有线性复杂性，并且在推断过程中的性能更有效。此外，我们发现与现有基于注意力的模型相比，我们的模型可以生成更多样化的输出，而背景化的功能冗余性较小。

我们在变压器中重新审视设计选择，并提出方法来解决它们在处理长序列中的弱点。首先，我们提出了一个名为“门控注意单元”的简单层，该层允许使用较弱的单头注意，而质量损失最小。然后，我们提出了一种与该新层的线性近似方法互补的，该方法对加速器友好且质量高度竞争。最终的型号（名为Flash）与短（512）和长（8K）上下文长度相匹配，在WIKI-40B上达到高达4.9 $ \ times $的训练速度和PG上的12.1 $ \ times $，在PG上达到了4.9 $ \ times $的困惑。-19用于自动回归语言建模，C4的4.8 $ \ times $用于掩盖语言建模。

基于变压器的预审前的语言模型（LMS）在自然语言的理解中无处不在，但由于其二次复杂性，无法应用于故事，科学文章和长文档等长序列。尽管已经提出了无数有效的变压器变体，但它们通常是基于需要从头开始的昂贵预处理的自定义实现。在这项工作中，我们提出了雪橇：滑动编码器和解码器，这是一种处理长序列的简单方法，可以重新使用和利用经过战斗测试的短文本预处理的LMS。具体而言，我们将输入分配到重叠的块中，用短文本LM编码器编码每个块，然后使用预审计的解码器将信息融合到跨块（Fusion-In-In-In-In-indecoder）之间。我们通过受控实验说明，雪橇提供了一种可行的策略，可以长期理解并评估我们在卷轴上的方法，这是一个基准，该基准在各种语言理解任务中具有七个数据集。我们发现，雪橇与高达50倍的专业型号具有竞争力，并且需要专用且昂贵的预处理步骤。

变形金刚在语言和视觉域中取得了成功。然而，将它们缩放到长期序列（例如长）或高分辨率图像，因为自我关注机构相对于输入序列长度具有二次时间和存储器复杂性。在本文中，我们提出了长短变压器（变压器-LS），是一种有效的自我关注机制，用于对语言和视觉任务进行线性复杂性建模的长序列。它用动态投影聚集了一种新的远程关注，以模拟远处相关性和短期注意，以捕获细粒度的局部相关性。我们提出了双重正径策略，以解释两个注意机制之间的规模不匹配。变压器-LS可以应用于自回归和双向模型，而无需额外复杂。我们的方法在语言和视觉域中的多个任务中优于最先进的模型，包括远程竞技场基准，自回归语言建模和想象成分类。例如，变换器-LS使用比以前的方法的一半在eNWIK8上实现0.97测试BPC，同时与其在同一硬件上的全部关注版本相比，可以更快地处理3倍。在Imagenet上，它可以获得最先进的结果（例如，适度大小的55.8M模型，仅在224x224 Imagenet-1K上培训，可以获得顶级1精度84.1％），同时在高分辨率上更加可扩展图片。源代码和模型在https://github.com/nvidia/transformer-ls上发布。

Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence length due to their full attention mechanism. To remedy this, we propose, BIGBIRD, a sparse attention mechanism that reduces this quadratic dependency to linear. We show that BIGBIRD is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to 8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context, BIGBIRD drastically improves performance on various NLP tasks such as question answering and summarization. We also propose novel applications to genomics data.

Transformer-based models have pushed state of the art in many areas of NLP, but our understanding of what is behind their success is still limited. This paper is the first survey of over 150 studies of the popular BERT model. We review the current state of knowledge about how BERT works, what kind of information it learns and how it is represented, common modifications to its training objectives and architecture, the overparameterization issue and approaches to compression. We then outline directions for future research.

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。

Transformer models have achieved superior performance in various natural language processing tasks. However, the quadratic computational cost of the attention mechanism limits its practicality for long sequences. There are existing attention variants that improve the computational efficiency, but they have limited ability to effectively compute global information. In parallel to Transformer models, state space models (SSMs) are tailored for long sequences, but they are not flexible enough to capture complicated local information. We propose SPADE, short for $\underline{\textbf{S}}$tate s$\underline{\textbf{P}}$ace $\underline{\textbf{A}}$ugmente$\underline{\textbf{D}}$ Transform$\underline{\textbf{E}}$r. Specifically, we augment a SSM into the bottom layer of SPADE, and we employ efficient local attention methods for the other layers. The SSM augments global information, which complements the lack of long-range dependency issue in local attention methods. Experimental results on the Long Range Arena benchmark and language modeling tasks demonstrate the effectiveness of the proposed method. To further demonstrate the scalability of SPADE, we pre-train large encoder-decoder models and present fine-tuning results on natural language understanding and natural language generation tasks.

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.

我们介绍了块状变压器，该变压器以序列的反复方式应用变压器层，并且相对于序列长度具有线性复杂性。我们的复发单元在训练过程中在代币的块而不是单个令牌上运行，并利用块内并行计算，以便有效利用加速器硬件。单元本身非常简单。它仅仅是一个变压器层：它使用自我注意事项和交叉注意力来有效计算大量状态向量和令牌上的复发函数。我们的设计部分受到LSTM单元的启发，它使用LSTM风格的大门，但它可以将典型的LSTM单元缩放为几个数量级。我们的复发实现在计算时间和参数计数中都具有相同的成本作为传统的变压器层，但是在很长的序列中，语言建模任务中的语言建模任务的困惑极大地改善了。我们的模型比远程变压器XL基线的表现宽大，同时运行的速度是两倍。我们证明了它在PG19（书籍），Arxiv论文和GitHub源代码上的有效性。我们的代码已发布为开​​源。

尽管在文档理解方面取得了成功，但由于计算中的几个挑战以及如何有效吸收长期多模式输入，因此长期文档理解的实际任务在很大程度上尚未探索。大多数基于变压器的方法仅处理简短的文档，并且由于其过度的计算和内存限制，因此仅使用文本信息来引起注意。为了解决长期文档理解中的这些问题，我们探索了处理1D和新的2D位置引人入胜的不同方法，并以本质上的背景缩短了。实验结果表明，我们提出的模型基于各种评估指标具有此任务的优势。此外，我们的模型仅对注意力进行更改，因此很容易适应任何基于变压器的体系结构。

The last several years have witnessed remarkable progress in video-and-language (VidL) understanding. However, most modern VidL approaches use complex and specialized model architectures and sophisticated pretraining protocols, making the reproducibility, analysis and comparisons of these frameworks difficult. Hence, instead of proposing yet another new VidL model, this paper conducts a thorough empirical study demystifying the most important factors in the VidL model design. Among the factors that we investigate are (i) the spatiotemporal architecture design, (ii) the multimodal fusion schemes, (iii) the pretraining objectives, (iv) the choice of pretraining data, (v) pretraining and finetuning protocols, and (vi) dataset and model scaling. Our empirical study reveals that the most important design factors include: temporal modeling, video-to-text multimodal fusion, masked modeling objectives, and joint training on images and videos. Using these empirical insights, we then develop a step-by-step recipe, dubbed VindLU, for effective VidL pretraining. Our final model trained using our recipe achieves comparable or better than state-of-the-art results on several VidL tasks without relying on external CLIP pretraining. In particular, on the text-to-video retrieval task, our approach obtains 61.2% on DiDeMo, and 55.0% on ActivityNet, outperforming current SOTA by 7.8% and 6.1% respectively. Furthermore, our model also obtains state-of-the-art video question-answering results on ActivityNet-QA, MSRVTT-QA, MSRVTT-MC and TVQA. Our code and pretrained models are publicly available at: https://github.com/klauscc/VindLU.

状态空间模型已显示在建模远距离依赖性方面有效，特别是序列分类任务。在这项工作中，我们着重于对英语书籍，GitHub源代码和Arxiv数学文章的自回旋序列建模。基于围绕封闭激活功能的有效性的最新发展，我们提出了一个名为“封闭状态空间（GSS）”的新层，并表明它的训练速度明显快于TPU的S4（即DSS）的对角线版本，具有相当竞争力 - 基于变压器的基线，并表现出零击向更长的输入，同时直接实施。最后，我们表明，利用自我意见来建模局部依赖性，可以进一步提高GSS的性能。