示范学习旨在通过在少数射击设置中提供回答的演示来指导及时的预测。尽管取得了令人鼓舞的结果，但现有工作仅将回答的示例与及时模板（包括原始上下文）相连，而无需任何其他操作，从而忽略了迅速示意的依赖性。此外，先前的研究发现，随机替换示威的标签极小地损害了性能，这表明该模型无法正确地了解示威活动所带来的知识。受到人类学习过程的启发，在本文中，我们引入了模仿演示学习（模仿），以通过明确模仿人类审查行为来加强演示学习，其中包括：（1）对比度学习机制，以专注于类似的演示。 （2）证明标签重新预测方法以合并已知知识。实验结果表明，我们提出的方法在14个分类中心中有11个实现了最先进的性能。进一步的研究还证明，模仿 - demo加强了迅速与示威之间的关联，这可以为探索示范学习的工作方式提供基础。

The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context. Inspired by their findings, we study few-shot learning in a more practical scenario, where we use smaller language models for which fine-tuning is computationally efficient. We present LM-BFF-better few-shot fine-tuning of language models 1 -a suite of simple and complementary techniques for finetuning language models on a small number of annotated examples. Our approach includes (1) prompt-based fine-tuning together with a novel pipeline for automating prompt generation; and (2) a refined strategy for dynamically and selectively incorporating demonstrations into each context. Finally, we present a systematic evaluation for analyzing few-shot performance on a range of NLP tasks, including classification and regression. Our experiments demonstrate that our methods combine to dramatically outperform standard fine-tuning procedures in this low resource setting, achieving up to 30% absolute improvement, and 11% on average across all tasks. Our approach makes minimal assumptions on task resources and domain expertise, and hence constitutes a strong task-agnostic method for few-shot learning. 2 * The first two authors contributed equally. 1 Alternatively, language models' best friends forever. 2 Our implementation is publicly available at https:// github.com/princeton-nlp/LM-BFF.

最近，与“预训练，及时和预测”的新范式相比，与“预训练，微调”范式相比，新的范式“预训练，及时和预测”取得了显着的成就。在基于及时的GPT-3成功之后，一系列基于蒙版的语言模型（MLM）（例如Bert，Roberta）及时学习方法变得流行并广泛使用。但是，另一个有效的预训练的判别模型Electra可能被忽略了。在本文中，我们尝试使用拟议的替换代替令牌检测（RTD）基于基于的及时学习方法来完成零摄像的几个NLP任务。实验结果表明，基于RTD-Prompt学习的Electra模型可达到令人惊讶的最先进的零拍性能。在数字上，与MLM-Roberta-Large和MLM-Bert-Large相比，我们的RTD-Electra-Large在所有15个任务上平均提高了约8.4％和13.7％。特别是在SST-2任务上，我们的RTD-Electra-Large在没有任何培训数据的情况下达到了令人惊讶的90.1％精度。总体而言，与预先训练的蒙版语言模型相比，预先训练的代替令牌检测模型在零拍学习中的性能更好。因此，Electra是一位出色的零球学习者。源代码可在以下网址获得：https：//github.com/nishiwen1214/rtd-electra。

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。

Pre-trained language models (PLMs) have exhibited remarkable few-shot learning capabilities when provided a few examples in a natural language prompt as demonstrations of test instances, i.e., in-context learning. However, the performance of in-context learning is susceptible to the choice of prompt format, training examples and the ordering of the training examples. In this paper, we propose a novel nearest-neighbor calibration framework for in-context learning to ease this issue. It is inspired by a phenomenon that the in-context learning paradigm produces incorrect labels when inferring training instances, which provides a useful supervised signal to calibrate predictions. Thus, our method directly augments the predictions with a $k$-nearest-neighbor ($k$NN) classifier over a datastore of cached few-shot instance representations obtained by PLMs and their corresponding labels. Then adaptive neighbor selection and feature regularization modules are introduced to make full use of a few support instances to reduce the $k$NN retrieval noise. Experiments on various few-shot text classification tasks demonstrate that our method significantly improves in-context learning, while even achieving comparable performance with state-of-the-art tuning-based approaches in some sentiment analysis tasks.

Prompt learning recently become an effective linguistic tool to motivate the PLMs' knowledge on few-shot-setting tasks. However, studies have shown the lack of robustness still exists in prompt learning, since suitable initialization of continuous prompt and expert-first manual prompt are essential in fine-tuning process. What is more, human also utilize their comparative ability to motivate their existing knowledge for distinguishing different examples. Motivated by this, we explore how to use contrastive samples to strengthen prompt learning. In detail, we first propose our model ConsPrompt combining with prompt encoding network, contrastive sampling module, and contrastive scoring module. Subsequently, two sampling strategies, similarity-based and label-based strategies, are introduced to realize differential contrastive learning. The effectiveness of proposed ConsPrompt is demonstrated in five different few-shot learning tasks and shown the similarity-based sampling strategy is more effective than label-based in combining contrastive learning. Our results also exhibits the state-of-the-art performance and robustness in different few-shot settings, which proves that the ConsPrompt could be assumed as a better knowledge probe to motivate PLMs.

大型预训练的语言模型（PLM）的最新进展导致了自然语言理解（NLU）任务的令人印象深刻的增长，并具有特定于任务的微调。但是，直接调整PLM在很大程度上依赖大量的标记实例，这些实例通常很难获得。迅速对PLM的调整已被证明对各种少数次任务很有价值。现有的作品研究基于迅速的NLU任务的基于及时的调整，主要集中于用语言器来得出正确的标签单词或生成及时的模板，以从PLM中启发语义。此外，还对常规数据增强方法进行了验证，可用于少量射击任务。但是，目前几乎没有针对基于及时的调整范式设计的数据增强方法。因此，我们研究了迅速的少数射击学习者的新数据增强问题。由于标签语义对于迅速的调整至关重要，因此我们提出了一种新颖的标签引导数据增强方法促进DA，该方法利用了丰富的标签语义信息以进行数据增强。很少的文本分类任务的广泛实验结果表明，我们提出的框架通过有效利用标签语义和数据扩展来实现自然语言理解来实现卓越的性能。

预先训练的蒙版语言模型通过将下游任务作为文本填充来成功执行几次学习。但是，作为全镜头环境中的强大替代方案，诸如Electra之类的判别预训练模型不适合范式。在这项工作中，我们调整了基于及时的几次学习来进行电信，并表明它在广泛的任务中优于蒙面的语言模型。Electra是预先训练的，以区分令牌是产生还是原始。我们自然地将其扩展到基于迅速的几次学习，通过培训来评分目标选项的原创性，而无需引入新参数。我们的方法很容易适应涉及多token预测的任务，而无需额外的计算开销。分析表明，Electra学习分布与下游任务更好。

及时调整是将预训练模型调整到下游任务的极其有效的工具。但是，基于标准及时的方法主要考虑下游任务的足够数据的情况。目前尚不清楚是否可以将优势传输到几杆式制度，在每个下游任务中只有有限的数据。尽管有些作品证明了在几次弹奏设置下及时调整的潜力，但通过搜索离散提示或使用有限数据调整软提示的主流方法仍然非常具有挑战性。通过广泛的实证研究，我们发现迅速调整和完全微调之间的学习差距仍然存在差距。为了弥合差距，我们提出了一个新的及时调整框架，称为软模板调整（STT）。 STT结合了手册和自动提示，并将下游分类任务视为掩盖语言建模任务。对不同设置的全面评估表明，STT可以在不引入其他参数的情况下缩小微调和基于及时的方法之间的差距。值得注意的是，它甚至可以胜过情感分类任务的时间和资源消耗的微调方法。

在本文中，我们描述了我们参与Case-2022的子任务1，即与休闲新闻语料库的事件因果关系识别。我们通过在少数带注释的示例（即几次配置）上利用一组简单但互补的技术来解决因果关系识别（CRI）任务。我们遵循一种基于迅速的预测方法，用于微调LMS，其中CRI任务被视为掩盖语言建模问题（MLM）。这种方法允许LMS在MLM问题上进行本地预先训练，可以直接生成对CRI特异性提示的文本响应。我们将此方法的性能与在整个数据集中训练的集合技术进行比较。我们表现​​最佳的提交仅接受了每班256个实例，整个数据集的一小部分培训，但能够获得第二好的精度（0.82），第三好的精度（0.82）和F1得分。 （0.85）非常接近获胜者团队（0.86）的报道。

在以前的作品中广泛讨论了句子语义相似性的原始伯特的表现不佳。我们发现不满意的性能主要是由于静态令牌嵌入偏差和无效的伯特层，而不是姓氏的高余弦相似性。为此，我们提出了一个迅速的句子嵌入方法，可以减少令牌嵌入偏差，使原始伯特层更有效。通过将句子嵌入式任务重新塑造为填充空白问题，我们的方法显着提高了原始伯特的性能。我们讨论了两个提示符，表示基于及时的句子嵌入的三个提示搜索方法。此外，我们提出了一种通过模板去噪技术的新型无监督培训目标，这大大缩短了监督和无人监督的环境之间的性能差距。对于实验，我们评估我们在非微调和微调的设置上的方法。即使是非微调方法也可以优于STS任务上的无监督服务器等微调的方法。我们的微调方法在无监督和监督设置中优于最先进的方法SIMCSE。与SIMCSE相比，我们分别在无监督环境下实现了2.29和2.58点的伯特和罗伯塔的改进。

提示将下游应用程序作为语言建模任务施放，与使用预训练的模型进行标准微调相比，已显示出样本有效的效率。但是，提示的一个陷阱是需要手动设计的模式，其结果可能是不直觉的，需要大量的验证集来调整。为了应对挑战，我们提出了一种全自动提示方法Autoseq：（1）我们在序列到序列模型上采用自然语言提示，从而实现自由形式生成和更大的标签搜索空间； （2）我们提出了标签序列 - 无限长度的短语以口头表达标签 - 这消除了手动模板的需求，并且比单个标签单词更具有表现力； （3）我们使用Beam Search自动生成大量的标签序列候选物，并提出对比度重新排列以获得最佳组合。 Autoseq显着胜过其他无手动设计方法，例如软提示调整，适配器调整和自动搜索单个标签单词；生成的标签序列比各种任务上的精选手动序列更好。我们的方法揭示了几次学习中序列模型的潜力，并阐明了通用通用和自动提示的途径。本文的源代码可以从https://github.com/thunlp/seq2seq-prompt获得。

Contrastive learning has become a new paradigm for unsupervised sentence embeddings. Previous studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. In this paper, we propose a novel Contrastive learning method with Prompt-derived Virtual semantic Prototypes (ConPVP). Specifically, with the help of prompts, we construct virtual semantic prototypes to each instance, and derive negative prototypes by using the negative form of the prompts. Using a prototypical contrastive loss, we enforce the anchor sentence embedding to be close to its corresponding semantic prototypes, and far apart from the negative prototypes as well as the prototypes of other sentences. Extensive experimental results on semantic textual similarity, transfer, and clustering tasks demonstrate the effectiveness of our proposed model compared to strong baselines. Code is available at https://github.com/lemon0830/promptCSE.

提示方法被认为是几次自然语言处理的关键进展之一。最近对基于离散令牌的``硬提示''转移到连续``软提示''的最新研究，这些提示将可学习的向量用作伪提示代币并实现更好的性能。尽管显示出有希望的前景，但观察到这些软宣传的方法在很大程度上依赖良好的初始化来生效。不幸的是，获得软提示的完美初始化需要了解内在语言模型的工作和精心设计，这绝非易事，必须从头开始重新启动每个新任务。为了解决此问题，我们提出了一种称为Metaprompting的广义软提示方法，该方法采用了良好认可的模型 - 静态元学习算法，以自动找到更好的及时初始化，从而快速适应新的促进任务。问题并在四个不同的数据集上带来了显着改善（1次设置的准确性提高了6分），从而实现了新的最新性能。

几乎没有命名的实体识别（NER）对于在有限的资源领域中标记的实体标记至关重要，因此近年来受到了适当的关注。现有的几声方法主要在域内设置下进行评估。相比之下，对于这些固有的忠实模型如何使用一些标记的域内示例在跨域NER中执行的方式知之甚少。本文提出了一种两步以理性为中心的数据增强方法，以提高模型的泛化能力。几个数据集中的结果表明，与先前的最新方法相比，我们的模型无形方法可显着提高跨域NER任务的性能，包括反事实数据增强和及时调用方法。我们的代码可在\ url {https://github.com/lifan-yuan/factmix}上获得。

Ultra-fine entity typing (UFET) predicts extremely free-formed types (e.g., president, politician) of a given entity mention (e.g., Joe Biden) in context. State-of-the-art (SOTA) methods use the cross-encoder (CE) based architecture. CE concatenates the mention (and its context) with each type and feeds the pairs into a pretrained language model (PLM) to score their relevance. It brings deeper interaction between mention and types to reach better performance but has to perform N (type set size) forward passes to infer types of a single mention. CE is therefore very slow in inference when the type set is large (e.g., N = 10k for UFET). To this end, we propose to perform entity typing in a recall-expand-filter manner. The recall and expand stages prune the large type set and generate K (K is typically less than 256) most relevant type candidates for each mention. At the filter stage, we use a novel model called MCCE to concurrently encode and score these K candidates in only one forward pass to obtain the final type prediction. We investigate different variants of MCCE and extensive experiments show that MCCE under our paradigm reaches SOTA performance on ultra-fine entity typing and is thousands of times faster than the cross-encoder. We also found MCCE is very effective in fine-grained (130 types) and coarse-grained (9 types) entity typing. Our code is available at \url{https://github.com/modelscope/AdaSeq/tree/master/examples/MCCE}.

With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few training examples. It has been a new trend exploring ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress, challenges, and future work in ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques of ICL, including training strategies, prompting strategies, and so on. Finally, we present the challenges of ICL and provide potential directions for further research. We hope our work can encourage more research on uncovering how ICL works and improving ICL in future work.

We present Pre-trained Machine Reader (PMR), a novel method to retrofit Pre-trained Language Models (PLMs) into Machine Reading Comprehension (MRC) models without acquiring labeled data. PMR is capable of resolving the discrepancy between model pre-training and downstream fine-tuning of existing PLMs, and provides a unified solver for tackling various extraction tasks. To achieve this, we construct a large volume of general-purpose and high-quality MRC-style training data with the help of Wikipedia hyperlinks and design a Wiki Anchor Extraction task to guide the MRC-style pre-training process. Although conceptually simple, PMR is particularly effective in solving extraction tasks including Extractive Question Answering and Named Entity Recognition, where it shows tremendous improvements over previous approaches especially under low-resource settings. Moreover, viewing sequence classification task as a special case of extraction task in our MRC formulation, PMR is even capable to extract high-quality rationales to explain the classification process, providing more explainability of the predictions.

Through in-context learning (ICL), large-scale language models are effective few-shot learners without additional model fine-tuning. However, the ICL performance does not scale well with the number of available training samples as it is limited by the inherent input length constraint of the underlying language model. Meanwhile, many studies have revealed that language models are also powerful feature extractors, allowing them to be utilized in a black-box manner and enabling the linear probing paradigm, where lightweight discriminators are trained on top of the pre-extracted input representations. This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. PALP inherits the scalability of linear probing and the capability of enforcing language models to derive more meaningful representations via tailoring input into a more conceivable form. Throughout in-depth investigations on various datasets, we verified that PALP significantly enhances the input representations closing the gap between ICL in the data-hungry scenario and fine-tuning in the data-abundant scenario with little training overhead, potentially making PALP a strong alternative in a black-box scenario.

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.