大规模的预训练的语言模型（PLM）以能够仅通过在提示中调节一些被称为示范的示威演示的情况而不明确调整为所需的下游任务而被称为示威的示威来解决任务。但是，这种过程（即，在文章中的学习）自然会高度依赖通常从外部数据集中选择的演示。在本文中，我们提出了自我生成的文化学习（SG-ICL），该学习生成了从PLM本身中的文化学习演示，以最大程度地减少对外部演示的依赖。我们对四个不同的文本分类任务进行实验，并显示SG-ICL的表现明显优于零射击学习，并且通常价值约0.6个黄金训练样本。此外，与培训数据集的随机选择相比，我们的生成的演示表现出更一致的性能，方差较低。

Recent studies have revealed the intriguing few-shot learning ability of pretrained language models (PLMs): They can quickly adapt to a new task when fine-tuned on a small amount of labeled data formulated as prompts, without requiring abundant task-specific annotations. Despite their promising performance, most existing few-shot approaches that only learn from the small training set still underperform fully supervised training by nontrivial margins. In this work, we study few-shot learning with PLMs from a different perspective: We first tune an autoregressive PLM on the few-shot samples and then use it as a generator to synthesize a large amount of novel training samples which augment the original training set. To encourage the generator to produce label-discriminative samples, we train it via weighted maximum likelihood where the weight of each token is automatically adjusted based on a discriminative meta-learning objective. A classification PLM can then be fine-tuned on both the few-shot and the synthetic samples with regularization for better generalization and stability. Our approach FewGen achieves an overall better result across seven classification tasks of the GLUE benchmark than existing few-shot learning methods, improving no-augmentation methods by 5+ average points, and outperforming augmentation methods by 3+ average points.

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.

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.

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.

Large language models have exhibited intriguing in-context learning capability, achieving promising zero- and few-shot performance without updating the parameters. However, conventional in-context learning is usually restricted by length constraints, rendering it ineffective to absorb supervision from a large number of examples. In order to go beyond few shots, we introduce structured prompting that breaks the length limit and scales in-context learning to thousands of examples. Specifically, demonstration examples are separately encoded with well-designed position embeddings, and then they are jointly attended by the test example using a rescaled attention mechanism. So we can scale the number of exemplars with linear complexity instead of quadratic complexity with respect to length. Experimental results on a diverse set of tasks show that our approach improves end-task performance and reduces evaluation variance over conventional in-context learning as the number of demonstration examples increases. Code has been released at https://aka.ms/structured-prompting.

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.

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.

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。

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.

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。

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。

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.

在维持预审预定序列模型的灵活性的同时，是否有利于常识性推理，这仍然是一个悬而未决的问题。为了调查这个问题，我们开发了生成的知识提示，该提示包括从语言模型中生成知识，然后在回答问题时提供知识作为附加输入。我们的方法不需要特定于任务的监督知识集成或访问结构化的知识库，但它可以提高四个常识性推理任务上的大规模，最先进的模型的性能，从而实现最先进-ART结果取决于数值常识（NumerSense），通用常识性（Commonsenseqa 2.0）和科学常识（QASC）基准。产生的知识促使大型语言模型是灵活的外部知识来源，以改善常识性推理。我们的代码可从https://github.com/liujch1998/gkp获得

已显示迅速学习可以在大多数文本分类任务中实现近调调节性能，但很少有培训示例。对于样品稀缺的NLP任务是有利的。在本文中，我们试图将其应用于实际情况，即恢复信息提取，并增强现有方法，以使其更适用于简历信息提取任务。特别是，我们根据简历的文本特征创建了多组手动模板和语言器。此外，我们比较了蒙版语言模型（MLM）预培训语言模型（PLM）和SEQ2SEQ PLM在此任务上的性能。此外，我们改进了口头设计的设计方法，用于知识渊博的及时调整，以便为其他基于应用程序的NLP任务的迅速模板和语言设计的设计提供了示例。在这种情况下，我们提出了手动知识渊博的语言器（MKV）的概念。构造与应用程序方案相对应的知识渊博的口头表的规则。实验表明，基于我们的规则设计的模板和言语器比现有的手动模板更有效，更强大，并自动生成及时方法。已经确定，当前可用的自动提示方法无法与手动设计的及时模板竞争一些现实的任务方案。最终混淆矩阵的结果表明，我们提出的MKV显着解决了样本不平衡问题。

Triplet extraction aims to extract entities and their corresponding relations in unstructured text. Most existing methods train an extraction model on high-quality training data, and hence are incapable of extracting relations that were not observed during training. Generalizing the model to unseen relations typically requires fine-tuning on synthetic training data which is often noisy and unreliable. In this paper, we argue that reducing triplet extraction to a template filling task over a pre-trained language model can equip the model with zero-shot learning capabilities and enable it to leverage the implicit knowledge in the language model. Embodying these ideas, we propose a novel framework, ZETT (ZEro-shot Triplet extraction by Template infilling), that is based on end-to-end generative transformers. Our experiments show that without any data augmentation or pipeline systems, ZETT can outperform previous state-of-the-art models with 25% less parameters. We further show that ZETT is more robust in detecting entities and can be incorporated with automatically generated templates for relations.

With the evergrowing sizes of pre-trained models (PTMs), it has been an emerging practice to only provide the inference APIs for users, namely model-as-a-service (MaaS) setting. To adapt PTMs with model parameters frozen, most current approaches focus on the input side, seeking for powerful prompts to stimulate models for correct answers. However, we argue that input-side adaptation could be arduous due to the lack of gradient signals and they usually require thousands of API queries, resulting in high computation and time costs. In light of this, we present Decoder Tuning (DecT), which in contrast optimizes task-specific decoder networks on the output side. Specifically, DecT first extracts prompt-stimulated output scores for initial predictions. On top of that, we train an additional decoder network on the output representations to incorporate posterior data knowledge. By gradient-based optimization, DecT can be trained within several seconds and requires only one PTM query per sample. Empirically, we conduct extensive natural language understanding experiments and show that DecT significantly outperforms state-of-the-art algorithms with a $10^3\times$ speed-up.

最近的自然语言理解进展（NLU）已经被驱动，部分是由胶水，超级格，小队等的基准。事实上，许多NLU模型现在在许多任务中匹配或超过“人类水平”性能这些基准。然而，大多数这些基准测试都提供模型访问相对大量的标记数据进行培训。因此，该模型提供了比人类所需的更多数据，以实现强大的性能。这有动机侧重于侧重于改善NLU模型的少量学习性能。然而，缺乏少量射门的标准化评估基准，导致不同纸张中的不同实验设置。为了帮助加速这一工作的工作，我们介绍了线索（受限制的语言理解评估标准），这是评估NLU模型的几次拍摄学习功能的基准。我们证明，虽然最近的模型在获得大量标记数据时达到人类性能，但对于大多数任务，少量拍摄设置中的性能存在巨大差距。我们还展示了几个拍摄设置中替代模型家族和适应技术之间的差异。最后，我们讨论了在设计实验设置时讨论了评估真实少量学习绩效的实验设置，并提出了统一的标准化方法，以获得少量学习评估。我们的目标是鼓励对NLU模型的研究，可以概括为具有少数示例的新任务。线索的代码和数据可以在https://github.com/microsoft/clues提供。

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.

最近的几种方法，例如参数有效的微调（PEFT）和模式开发训练（PET），在标签筛选设置中取得了令人印象深刻的结果。但是，它们很难使用，因为它们会受到手动制作的提示的高度可变性，并且通常需要十亿参数语言模型才能达到高精度。为了解决这些缺点，我们提出了SETFIT（句子变压器微调），这是一个有效且迅速的框架，用于对句子变形金刚（ST）进行几次微调。 SetFit首先以对比的暹罗方式对少数文本对进行微调验证的st。然后将所得模型用于生成丰富的文本嵌入，这些嵌入方式用于训练分类头。这个简单的框架不需要任何提示或口头化，并且比现有技术少的参数较少，因此可以实现高精度。我们的实验表明，SetFit通过PEFT和PET技术获得了可比的结果，同时训练的速度更快。我们还表明，SETFIT可以通过简单地切换ST主体来应用于多语言设置。我们的代码可从https://github.com/huggingface/setFit以及我们的数据集获得，网址为https://huggingface.co/setfit。