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.

非常大的预培训的语言模型（PTM）（如GPT-3）通常被释放为服务，允许用户设计特定于任务的提示以通过一些黑盒API查询PTMS。在这样的场景中，我们调用语言模型 - AS-Service（LMAAS），PTM的梯度通常不可用。我们可以通过仅访问模型推断API来优化任务提示吗？基于最近的观察结果，大型PTMS具有非常低的内在维度，这项工作提出了黑匣子调谐，通过无衍生算法优化PTM。特别是，我们通过迭代调用PTM推断API来调用CMA-es以优化预先提示的连续提示。我们的实验结果表明，黑匣子调整罗伯塔在少数标签样本上不仅显着优于手动提示和GPT-3的上下文学习，而且还超越了基于梯度的对应物，即提示调整和完整的模型调整。

We describe PromptBoosting, a query-efficient procedure for building a text classifier from a neural language model (LM) without access to the LM's parameters, gradients, or hidden representations. This form of "black-box" classifier training has become increasingly important as the cost of training and inference in large-scale LMs grows. But existing black-box LM classifier learning approaches are themselves computationally inefficient, typically specializing LMs to the target task by searching in a large space of (discrete or continuous) prompts using zeroth-order optimization methods. Instead of directly optimizing in prompt space, PromptBoosting obtains a small pool of prompts via a gradient-free approach and then constructs a large pool of weak learners by pairing these prompts with different elements of the LM's output distribution. These weak learners are then ensembled using the AdaBoost algorithm. The entire learning process requires only a small number of forward passes and no backward pass. Experiments show that PromptBoosting achieves state-of-the-art performance in multiple black-box few-shot classification tasks, and matches or outperforms full fine-tuning in both few-shot and standard learning paradigms, while training 10x faster than existing black-box methods.

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。

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.

快速学习已成为现代自然语言处理的新范式，它直接适应培训的语言模型（PLMS）到$ CLOZE $ -Style预测，自回归建模或序列到序列生成，从而导致各种任务的表现。但是，尚未提出及时学习的标准实施框架，以及大多数现有的及时学习码条，通常是不受管制的，仅为特定方案提供有限的实现。由于有许多细节，例如模板策略，初始化策略和语言化策略等，因此需要在快速学习中考虑，从业者面临障碍，以便快速调整所需的迅速学习方法到他们的应用程序。在本文中，我们展示了{OpenPrompt}，一个统一的易于使用的工具包，可以通过PLMS快速学习。 OpenPrompt是一项研究型框架，配备了效率，模块化和可扩展性，其组合性允许自由地将不同的PLMS，任务格式和提示模块组合在统一的范例中。用户可以宽松地部署快速学习框架，并在没有约束的情况下在不同的NLP任务上评估它们的泛化。 OpenPrompt在{\ url {https://github.com/thunlp/openprompt}}上公开发布。

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.

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。

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。

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。

Prompt tuning recently becomes a hot-spot in the applications of large pretrained language models on specific downstream tasks. Regarding the Language Model as a Service (LMaaS), black-box tuning using derivative-free optimization (DFO) provides a novel approach to expand the practical scenarios of pretrained models and enrich the researches of few-shot learning. In this report, we present our solution in this competition that is based on the LMaaS scenario. Our solution consists of several modifications to BBTv2, including multiple label words, selection of P0, rolling update strategy, multi-task loss from MLP classifier, and finally using the ensemble method to further improve generalization ability. We also shared some strategies that we tried but didn't use in the final submission for further discussion. In the end we raised a question about the SNLI dataset and the impact on the results, as well as our concerns about the competition.

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。

几乎没有射击的内在学习（ICL）使预训练的语言模型能够通过为输入的一部分提供少量的培训示例来执行以前的任务，而无需任何基于梯度的培训。 ICL会产生大量的计算，内存和存储成本，因为它每次进行预测时都涉及处理所有培训示例。参数有效的微调（PEFT）（例如，适配器模块，提示调谐，稀疏更新方法等）提供了替代范式，其中训练了一组少量参数以启用模型来执行新任务。在本文中，我们严格地比较了几个ICL和PEFT，并证明后者提供了更好的准确性，并大大降低了计算成本。在此过程中，我们引入了一种称为（IA）$^3 $的新PEFT方法，该方法通过学习的向量来扩展激活，从而获得更强的性能，同时仅引入相对少量的新参数。我们还提出了一个基于称为T-FEW的T0模型的简单食谱，可以将其应用于新任务，而无需特定于任务的调整或修改。我们通过将T-FEW应用于木筏基准，首次实现超人性能，并以6％的绝对性能优于最先进的方法来验证T-FEW对完全看不见的任务的有效性。我们实验中使用的所有代码均可公开使用。

In this work, we explore "prompt tuning," a simple yet effective mechanism for learning "soft prompts" to condition frozen language models to perform specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signals from any number of labeled examples. Our end-to-end learned approach outperforms GPT-3's few-shot learning by a large margin. More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method "closes the gap" and matches the strong performance of model tuning (where all model weights are tuned). This finding is especially relevant because large models are costly to share and serve and the ability to reuse one frozen model for multiple downstream tasks can ease this burden. Our method can be seen as a simplification of the recently proposed "prefix tuning" of Li and Liang (2021) and we provide a comparison to this and other similar approaches. Finally, we show that conditioning a frozen model with soft prompts confers benefits in robustness to domain transfer and enables efficient "prompt ensembling." * Work done as a Google AI Resident.

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。

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.

Pre-trained large language models can efficiently interpolate human-written prompts in a natural way. Multitask prompted learning can help generalization through a diverse set of tasks at once, thus enhancing the potential for more effective downstream fine-tuning. To perform efficient multitask-inference in the same batch, parameter-efficient fine-tuning methods such as prompt tuning have been proposed. However, the existing prompt tuning methods may lack generalization. We propose SPT, a semi-parametric prompt tuning method for multitask prompted learning. The novel component of SPT is a memory bank from where memory prompts are retrieved based on discrete prompts. Extensive experiments, such as (i) fine-tuning a full language model with SPT on 31 different tasks from 8 different domains and evaluating zero-shot generalization on 9 heldout datasets under 5 NLP task categories and (ii) pretraining SPT on the GLUE datasets and evaluating fine-tuning on the SuperGLUE datasets, demonstrate effectiveness of SPT.

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.

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.