我们提供了从文本到文本变换器（T5）的第一次探索句子嵌入式。句子嵌入式广泛适用于语言处理任务。虽然T5在作为序列到序列映射问题的语言任务上实现令人印象深刻的性能，但目前尚不清楚如何从编码器解码器模型生成陈列嵌入的句子。我们调查三种方法提取T5句子嵌入方法：两个仅利用T5编码器，一个使用全T5编码器解码器模型。为了支持我们的调查，我们建立了一个新的句子代表转移基准，SentGlue，它将Senteval Toolkit扩展到粘合基准的九个任务。我们的编码器的型号优于Senteval和SentGlue传输任务的句子 - BERT和SIMCSE句子嵌入，包括语义文本相似性（STS）。发现从数百万到数十亿参数的缩放T5产生一致的进一步改进。最后，我们的编码器 - 解码器方法在使用句子嵌入时在STS上实现了新的最先进的。我们的模型在https://tfhub.dev/google/collections/sentence-t5/1发布。

已经表明，在一个域上训练的双编码器经常概括到其他域以获取检索任务。一种广泛的信念是，一个双编码器的瓶颈层，其中最终得分仅仅是查询向量和通道向量之间的点产品，它过于局限，使得双编码器是用于域外概括的有效检索模型。在本文中，我们通过缩放双编码器模型的大小{\ em同时保持固定的瓶颈嵌入尺寸固定的瓶颈的大小来挑战这一信念。令人惊讶的是，令人惊讶的是，缩放模型尺寸会对各种缩放提高检索任务，特别是对于域外泛化。实验结果表明，我们的双编码器，\ textbf {g} enovalizable \ textbf {t} eTrievers（gtr），优先级％colbert〜\ cite {khattab2020colbertt}和现有的稀疏和密集的索取Beir DataSet〜\ Cite {Thakur2021Beir}显着显着。最令人惊讶的是，我们的消融研究发现，GTR是非常数据的高效，因为它只需要10 \％MARCO监督数据，以实现最佳域的性能。所有GTR模型都在https://tfhub.dev/google/collections/gtr/1发布。

虽然对比学习大大提升了句子嵌入的表示，但它仍然受到现有句子数据集的大小的限制。在本文中，我们向Transaug（转换为增强），它提供了利用翻译句子对作为文本的数据增强的第一次探索，并介绍了两级范例，以提高最先进的句子嵌入。我们不是采用以其他语言设置培训的编码器，我们首先从SIMCSE编码器（以英语预先预先预订）蒸发蒸馏出一个汉语编码器，以便它们的嵌入在语义空间中靠近，这可以被后悔作为隐式数据增强。然后，我们只通过交叉语言对比学习更新英语编码器并将蒸馏的中文编码器冷冻。我们的方法在标准语义文本相似度（STS）上实现了一种新的最先进的，表现出SIMCSE和句子T5，以及由Senteval评估的传输任务的相应轨道中的最佳性能。

This paper presents SimCSE, a simple contrastive learning framework that greatly advances state-of-the-art sentence embeddings. We first describe an unsupervised approach, which takes an input sentence and predicts itself in a contrastive objective, with only standard dropout used as noise. This simple method works surprisingly well, performing on par with previous supervised counterparts. We find that dropout acts as minimal data augmentation, and removing it leads to a representation collapse. Then, we propose a supervised approach, which incorporates annotated pairs from natural language inference datasets into our contrastive learning framework by using "entailment" pairs as positives and "contradiction" pairs as hard negatives. We evaluate SimCSE on standard semantic textual similarity (STS) tasks, and our unsupervised and supervised models using BERT base achieve an average of 76.3% and 81.6% Spearman's correlation respectively, a 4.2% and 2.2% improvement compared to the previous best results. We also show-both theoretically and empirically-that the contrastive learning objective regularizes pre-trained embeddings' anisotropic space to be more uniform, and it better aligns positive pairs when supervised signals are available. 1 2 We randomly sample 10 6 sentences from English Wikipedia and fine-tune BERTbase with learning rate = 3e-5, N = 64. In all our experiments, no STS training sets are used.

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).

尽管在训练有素的语言模型方面取得了进展，但缺乏用于预训练的句子表示的统一框架。因此，它要求针对特定方案采用不同的预训练方法，并且预培训的模型可能受到其普遍性和表示质量的限制。在这项工作中，我们扩展了最近提出的MAE风格的预训练策略RELOMAE，以便可以有效地支持各种句子表示任务。扩展的框架由两个阶段组成，在整个过程中进行了逆转录。第一阶段对通用语料库进行了逆转，例如Wikipedia，BookCorpus等，从中可以从中学习基本模型。第二阶段发生在特定于领域的数据上，例如Marco和NLI，在该数据中，基本模型是基于逆转和对比度学习的。这两个阶段的训练前输出可能会服务于不同的应用，其有效性通过全面的实验验证。具体来说，基本模型被证明对零射击检索有效，并且在贝尔基准上取得了显着的性能。继续进行预训练的模型进一步受益于更多的下游任务，包括针对马可女士的特定领域的密集检索，自然问题以及句子嵌入式标准STS的质量和延性端的转移任务。这项工作的经验见解可能会激发预训练的句子代表的未来设计。我们的预培训模型和源代码将发布给公共社区。

This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a wide range of tasks. The model is trained in a contrastive manner with weak supervision signals from our curated large-scale text pair dataset (called CCPairs). E5 can be readily used as a general-purpose embedding model for any tasks requiring a single-vector representation of texts such as retrieval, clustering, and classification, achieving strong performance in both zero-shot and fine-tuned settings. We conduct extensive evaluations on 56 datasets from the BEIR and MTEB benchmarks. For zero-shot settings, E5 is the first model that outperforms the strong BM25 baseline on the BEIR retrieval benchmark without using any labeled data. When fine-tuned, E5 obtains the best results on the MTEB benchmark, beating existing embedding models with 40x more parameters.

Using a single model across various tasks is beneficial for training and applying deep neural sequence models. We address the problem of developing generalist representations of text that can be used to perform a range of different tasks rather than being specialised to a single application. We focus on processing short questions and developing an embedding for these questions that is useful on a diverse set of problems, such as question topic classification, equivalent question recognition, and question answering. This paper introduces QBERT, a generalist model for processing questions. With QBERT, we demonstrate how we can train a multi-task network that performs all question-related tasks and has achieved similar performance compared to its corresponding single-task models.

已经证明了对比学习适合学习句子嵌入，可以显着提高语义文本相似性（STS）任务。最近，大型对比学习模型，例如句子T5倾向于学到更强大的句子嵌入。虽然有效，但由于计算资源或时间成本限制，这种大型型号很难在线服务。为了解决这个问题，通常采用知识蒸馏（KD），这可以将大型“教师”模型压缩成一个小的“学生”模型，但通常会遭受一些性能损失。在这里，我们提出了一个增强的KD框架，称为蒸馏 - 对比度（迪斯科）。所提出的迪斯科框架首先利用KD将大句子嵌入模型的能力转移到大型未标记数据的小学生模型，然后在标记的训练数据上具有对比学习的学生模型。对于迪斯科舞厅的KD进程，我们进一步提出了对比的知识蒸馏（CKD），以增强教师模型培训，KD和学生模型的一致性，这可能会提高迅速学习的表现。 7 STS基准测试的广泛实验表明，使用所提出的迪斯科和CKD培训的学生模型很少或甚至没有性能损失，并且始终如一地优于相同参数大小的相应对应物。令人惊讶的是，我们的110米学生模型甚至可以优于最新的最新（SOTA）模型，即句子T5（11B），只有1％的参数。

Contrastive learning has been successfully used for retrieval of semantically aligned sentences, but it often requires large batch sizes or careful engineering to work well. In this paper, we instead propose a generative model for learning multilingual text embeddings which can be used to retrieve or score sentence pairs. Our model operates on parallel data in $N$ languages and, through an approximation we introduce, efficiently encourages source separation in this multilingual setting, separating semantic information that is shared between translations from stylistic or language-specific variation. We show careful large-scale comparisons between contrastive and generation-based approaches for learning multilingual text embeddings, a comparison that has not been done to the best of our knowledge despite the popularity of these approaches. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval -- the last of which we introduce in this paper. Overall, our Variational Multilingual Source-Separation Transformer (VMSST) model outperforms both a strong contrastive and generative baseline on these tasks.

绑架性自然语言推断（\ alpha {} nli）的任务是确定哪种假设是一组观察的可能性更可能的解释，是NLI的特别困难类型。与其仅仅确定因果关系，还需要常识，还需要评估解释的合理性。所有最新的竞争系统都以情境化表示为基础，并利用变压器体系结构来学习NLI模型。当某人面对特定的NLI任务时，他们需要选择可用的最佳模型。这是一项耗时且资源浓厚的努力。为了解决这个实用问题，我们提出了一种简单的方法来预测性能，而无需实际调整模型。我们通过测试预先训练的模型在\ alpha {} NLI任务上的性能如何，仅将具有余弦相似性的句子嵌入到训练这些嵌入式的分类器时所达到的性能。我们表明，余弦相似方法的准确性与Pearson相关系数为0.65的分类方法的准确性密切相关。由于相似性计算是在给定数据集上计算的数量级（少于一分钟与小时），因此我们的方法可以在模型选择过程中节省大量时间。

We present Relational Sentence Embedding (RSE), a new paradigm to further discover the potential of sentence embeddings. Prior work mainly models the similarity between sentences based on their embedding distance. Because of the complex semantic meanings conveyed, sentence pairs can have various relation types, including but not limited to entailment, paraphrasing, and question-answer. It poses challenges to existing embedding methods to capture such relational information. We handle the problem by learning associated relational embeddings. Specifically, a relation-wise translation operation is applied to the source sentence to infer the corresponding target sentence with a pre-trained Siamese-based encoder. The fine-grained relational similarity scores can be computed from learned embeddings. We benchmark our method on 19 datasets covering a wide range of tasks, including semantic textual similarity, transfer, and domain-specific tasks. Experimental results show that our method is effective and flexible in modeling sentence relations and outperforms a series of state-of-the-art sentence embedding methods. https://github.com/BinWang28/RSE

句子嵌入方法有许多成功的应用。但是，根据监督信号，在结果句子嵌入中捕获了哪些属性。在本文中，我们专注于具有相似体系结构和任务的两种类型的嵌入方法：一种关于自然语言推理任务的微型预训练的语言模型，以及其他微型训练的训练语言模型在单词预测任务上根据其定义句子，并研究其属性。具体而言，我们使用两个角度分区的STS数据集比较他们在语义文本相似性（STS）任务上的性能：1）句子源和2）句子对的表面相似性，并在下游和探测任务上比较其表现。此外，我们尝试结合两种方法，并证明将两种方法组合起来比无监督的STS任务和下游任务的各自方法的性能要好得多。

密集的段落检索旨在根据查询和段落的密集表示（即矢量）从大型语料库中检索查询的相关段落。最近的研究探索了改善预训练的语言模型，以提高密集的检索性能。本文提出了COT-MAE（上下文掩盖自动编码器），这是一种简单而有效的生成性预训练方法，可用于密集通道检索。 COT-MAE采用了不对称的编码器架构，该体系结构学会通过自我监督和上下文监督的掩盖自动编码来将句子语义压缩到密集的矢量中。精确，自我监督的掩盖自动编码学会学会为文本跨度内的令牌的语义建模，并学习上下文监督的蒙版自动编码学学习以建模文本跨度之间的语义相关性。我们对大规模通道检索基准进行实验，并显示出对强基础的大量改进，证明了COT-MAE的效率很高。

预训练的语言模型（PLM）在自然语言理解中的许多下游任务中取得了显着的性能增长。已提出了各种中文PLM，以学习更好的中文表示。但是，大多数当前模型都使用中文字符作为输入，并且无法编码中文单词中包含的语义信息。虽然最近的预训练模型同时融合了单词和字符，但它们通常会遭受不足的语义互动，并且无法捕获单词和字符之间的语义关系。为了解决上述问题，我们提出了一个简单而有效的PLM小扣手，该小扣子采用了对单词和性格表示的对比度学习。特别是，Clower通过对多透明信息的对比学习将粗粒的信息（即单词）隐式编码为细粒度表示（即字符）。在现实的情况下，小电动器具有很大的价值，因为它可以轻松地将其纳入任何现有的基于细粒的PLM中而无需修改生产管道。在一系列下游任务上进行的扩展实验表明，小动物的卓越性能超过了几个最先进的实验 - 艺术基线。

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.

尽管最近的多任务学习和自然语言处理的转移学习成功（NLP），但很少有效地研究了在训练中缩放任务数量的效果。迈出了这一目标，介绍了Exmix（极端混合物）：跨越各个领域和任务家庭的大规模收集107个监督的NLP任务。使用EXMIX，我们研究了最大规模的多任务预培训的影响，并分析了普通任务家庭之间的共同培训转移。通过此分析，我们表明手动策划用于多任务预训练的理想任务，并不简单，而且多任务缩放可以自行改进模型。最后，我们提出了Ext5：使用自我监督跨度去噪和监督EXMIX的多任务目标预先训练的模型。通过广泛的实验，我们表明Ext5优于超级格，宝石，彩虹，封闭书QA任务的强大T5基线，以及Exmix之外的几个任务。 Ext5在预训练时也显着提高了样品效率。

探索大规模预处理的基础模型对计算机视觉具有重大兴趣，因为这些模型可以快速转移到许多下游任务中。本文介绍了对比字幕（COCA），这是一种极简主义的设计，旨在为图像文本编码器编码器基础模型预算与对比度损失和字幕损失，从而从剪辑和诸如simvlm之类的生成方法之类的对比方法中包含模型能力。与所有解码器层都参与编码器输出的标准编码器 - 模块变压器相反，可口可乐省略了解码器层的上半部分的交叉注意，以编码单峰文本表示，并串联到剩余的解码器层，这些解码器与图像编码器相交的解码器层多模式图像文本表示。除了对多模态解码器输出的字幕损失外，我们还应用了单峰图像和文本嵌入之间的对比损失，该输出可以预测文本令牌自动加压。通过共享相同的计算图，可以用最小的开销有效地计算两个培训目标。可口可乐是端到端和从头开始的网络尺度alt-text数据和带注释的图像，通过将所有标签视为文本，无缝地统一自然语言监督以进行表示。从经验上讲，可口可乐通过零拍传输或在广泛的下游任务上进行零摄像转移或最少的特定任务适应，跨越视觉识别（Imagenet，Kinetics-400/600/700，瞬间， ），交叉模式检索（MSCOCO，FLICKR30K，MSR-VTT），多模式理解（VQA，SNLI-VE，NLVR2）和图像字幕（MSCOCO，NOCAPS）。值得注意的是，在Imagenet分类方面，COCA获得了86.3％的TOP-1准确性，带有冷冻编码器和学习的分类头90.6％，以及带有填充编码器的Imagenet上的新最先进的91.0％Top-1 Top-1精度。

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。

This paper presents a pre-training technique called query-as-context that uses query prediction to improve dense retrieval. Previous research has applied query prediction to document expansion in order to alleviate the problem of lexical mismatch in sparse retrieval. However, query prediction has not yet been studied in the context of dense retrieval. Query-as-context pre-training assumes that the predicted query is a special context for the document and uses contrastive learning or contextual masked auto-encoding learning to compress the document and query into dense vectors. The technique is evaluated on large-scale passage retrieval benchmarks and shows considerable improvements compared to existing strong baselines such as coCondenser and CoT-MAE, demonstrating its effectiveness. Our code will be available at https://github.com/caskcsg/ir/tree/main/cotmae-qc .