This work introduces a new multi-task, parameter-efficient language model (LM) tuning method that learns to transfer knowledge across different tasks via a mixture of soft prompts-small prefix embedding vectors pre-trained for different tasks. Our method, called ATTEMPT (ATTEntional Mixtures of Prompt Tuning), obtains source prompts as encodings of large-scale source tasks into a small number of parameters and trains an attention module to interpolate the source prompts and a newly initialized target prompt for every instance in the target task. During training, only the target task prompt and the attention weights, which are shared between tasks in multi-task training, are updated, while the original LM and source prompts are intact. ATTEMPT is highly parameter-efficient (e.g., updates 2,300 times fewer parameters than full fine-tuning) while achieving high task performance using knowledge from high-resource tasks. Moreover, it is modular using pre-trained soft prompts, and can flexibly add or remove source prompts for effective knowledge transfer. Our experimental results across 21 diverse NLP datasets show that ATTEMPT significantly outperforms prompt tuning and outperforms or matches fully fine-tuned or other parameter-efficient tuning approaches that use over ten times more parameters. Finally, ATTEMPT outperforms previous work in few-shot learning settings.

及时调整是以参数有效的方式对预训练的预训练语言模型的新范式。在这里，我们探讨了超级核武器的使用来产生超预价：我们提出了HyperPrompt，这是一种用于迅速基于变形金刚自我注意的任务调节的新型体系结构。超预要是通过超网络通过一代人来学习的端到端。 HyperPrompt允许网络学习特定于任务的功能地图，其中超预告是要参与的查询的任务全局记忆，同时启用了任务之间的灵活信息共享。我们表明，HyperPrompt与强大的多任务学习基线具有竞争力，其额外的任务条件参数的$ 0.14 \％$ $ \％，实现了出色的参数和计算效率。通过广泛的经验实验，我们证明，超级启示可以比强大的T5多任务学习基准和参数效率高效的适配器变体获得卓越的性能，包括及时调整和SuplyFormer ++在许多模型尺寸的自然语言理解胶水和SuperGrue的基准上。

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.

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.

通过微调将大规模的预训练语言模型适应下游任务是实现NLP基准测试最先进性能的标准方法。然而，微调具有数百万或数十亿个参数的所有重量模型是对低资源设置中不稳定的采样低效，并且浪费，因为它需要为每个任务存储模型的单独副本。最近的工作已经开发了参数高效的微调方法，但这些方法仍然需要相对大量的参数或表现不足标准微调。在这项工作中，我们提出了一种特殊调整大型语言模型的方法，其在任务性能和比率参数之间具有更好的权衡的方法，而不是比上事先工作。 Compacter通过构建适配器，低级优化和参数化超复分乘法层的思想之上来实现这一目标。具体地，Compacter将特定于特定的权重矩阵插入到预估计模型的权重中，这些权重被有效地计算为共享的“慢速”权重和“快速”等级 - 每个Compacter层定义的矩阵之间的矩阵产品的总和。仅通过培训0.047％的预磨料模型的参数，Compacter会在胶水上标准微调和胜过标准微调的标准微调和低资源设置。我们的代码在〜\ url {https://github.com/rabeehk/compacter}上公开使用。

最近，在大型文本语料库上预先培训的微调语言模型已经为Vision-and Langual（V＆L）任务以及纯语言任务提供了巨大的改进。但是，微调预训练模型的整个参数集变得不切实际，因为模型大小正在快速增长。因此，在本文中，我们将基于适配器的参数高效转移学习技术引入VL-BART和VL-T5等V＆L型号。我们在四个不同V＆L任务的统一多任务设置中评估我们的方法：VQAV2，GQA，NLVR2和MSCOCO图像标题。通过仔细的培训和彻底的实验，我们将三种流行的基于适配器的方法（适配器，Hyperformer，Compacter）基准，抵御标准的全部微调和最近提出的及时调整方法。我们还通过分享其权重以获得跨任务的知识来增强适配器的效率和性能。我们的结果表明，使用权重共享技术（总参数的4.4％）培训适配器可以匹配微调整个模型的性能。最后，我们提出了一个全面的分析，包括适配器和任务特定提示的组合以及V＆L对适配器进行培训的影响。我们的代码可用于：https://github.com/ylsung/vl_adapter。

最近在各种领域中采用了关于下游任务的大型预训练模型。但是，更新大型预训练模型的整个参数集是昂贵的。尽管最近提出的参数效率转移学习（PETL）技术允许在预先训练的骨干网络内更新一小部分参数（例如，仅使用2％的参数）用于新任务，但它们只能通过最多减少训练记忆要求30％。这是因为可训练参数的梯度计算仍然需要通过大型预训练的骨干模型反向传播。为了解决这个问题，我们提出了梯子侧调（LST），这是一种新的PETL技术，可将训练记忆要求减少更多。与现有的参数效率方法不同，将其他参数插入骨干网络中，我们训练梯子侧网络，梯子侧网络是一个小而独立的网络，将中间激活作为通过快速连接（梯子）从骨干网络中获得的输入作为输入，并进行预测。 LST的内存要求明显低于以前的方法，因为它不需要通过骨干网络反向传播，而是仅通过侧网和梯子连接。我们使用NLP（胶）和视觉语言（VQA，GQA，NLVR2，MSCOCO）任务上的各种模型（T5，CLIP-T5）进行评估。 LST节省了69％的内存成本来微调整个网络，而其他方法仅将其中的26％保存在相似的参数使用中（因此，更多的内存节省了2.7倍）。此外，LST在低内存状态下的适配器和洛拉的精度高。为了进一步显示这种更好的记忆效率的优势，我们还将LST应用于较大的T5型号（T5-Large，T5-3B），比完整的微调和其他PETL方法获得更好的胶水性能。我们对VL任务的实验也完全相同。

Parameter-efficient fine-tuning (PEFT) methods can adapt large language models to downstream tasks by training a small amount of newly added parameters. In multi-task settings, PEFT adapters typically train on each task independently, inhibiting transfer across tasks, or on the concatenation of all tasks, which can lead to negative interference. To address this, Polytropon (Ponti et al.) jointly learns an inventory of PEFT adapters and a routing function to share variable-size sets of adapters across tasks. Subsequently, adapters can be re-combined and fine-tuned on novel tasks even with limited data. In this paper, we investigate to what extent the ability to control which adapters are active for each task leads to sample-efficient generalization. Thus, we propose less expressive variants where we perform weighted averaging of the adapters before few-shot adaptation (Poly-mu) instead of learning a routing function. Moreover, we introduce more expressive variants where finer-grained task-adapter allocation is learned through a multi-head routing function (Poly-S). We test these variants on three separate benchmarks for multi-task learning. We find that Poly-S achieves gains on all three (up to 5.3 points on average) over strong baselines, while incurring a negligible additional cost in parameter count. In particular, we find that instruction tuning, where models are fully fine-tuned on natural language instructions for each task, is inferior to modular methods such as Polytropon and our proposed variants.

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。

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。

当前的Modus Operandi在改编预训练的模型中涉及更新所有骨干参数，即，完整的微调。本文介绍了视觉及时调整（VPT），作为视觉中大规模变压器模型的全面微调的有效替代方案。VPT从最近有效地调整大型语言模型的最新进展中汲取灵感，在输入空间中仅引入了少量的可训练参数（少于模型参数），同时保持模型骨架冻结。通过对各种下游识别任务的广泛实验，我们表明VPT与其他参数有效调整协议相比获得了显着的性能增长。最重要的是，在许多情况下，VPT甚至在模型能力和培训数据量表的许多情况下都胜过全面的微调，同时降低了每任务的存储成本。

本文探讨了提高语言模型的零次学习能力的简单方法。我们表明，指令调整 - 通过对说明书中所述的任务集合微调语言模型 - 大幅提升零射门上看不见任务中的表现。我们采取预训练的语言模型和指令调整它通过自然语言指令模板语言表达了60NLP任务137B参数。我们评估这种指令调整模型，我们称之为FLAN，在看不见的任务类型。FLAN显着改善其未修饰的对应的性能和超过25的20个任务，我们评估零射门175BGPT-3。FLAN甚至GPT-3通过在安利，RTE，BoolQ，AI2-ARC，OpenbookQA和StoryCloze大比分胜过几拍。消融研究显示任务和模型的规模，这个数字是指令调整取得成功的关键组成部分。

Recently, a large number of tuning strategies have been proposed to adapt pre-trained language models to downstream tasks. In this paper, we perform an extensive empirical evaluation of various tuning strategies for multilingual learning, particularly in the context of text summarization. Specifically, we explore the relative advantages of three families of multilingual tuning strategies (a total of five models) and empirically evaluate them for summarization over 45 languages. Experimentally, we not only established a new state-of-the-art on the XL-Sum dataset but also derive a series of observations that hopefully can provide hints for future research on the design of multilingual tuning strategies.

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.

提示调整（PT）是一个有前途的参数高效的方法，可以利用极大的预先培训的语言模型（PLM），它可以通过仅调整几个软提示来实现与全参数微调的可比性。但是，与微调相比，PT经验需要更多的培训步骤。为了探索我们通过重用培训的软提示和分享知识来提高PT的效率，我们经验探讨了在不同任务和模型中的软提示的可转换性。在交叉任务传输中，我们发现训练有素的软提示可以转移到类似的任务并初始化PT，以加速培训并提高性能。此外，为了探讨影响的因素，提示跨任务的可转移性，我们调查如何测量提示相似性，并发现激活神经元的重叠率与可转移性高度相关。在跨模型传输中，我们探索如何将PLM的提示投影到另一个PLM并成功培训了一种可以在类似任务上实现非琐碎的传输性能的投影仪。但是，使用预计提示初始化PT不起作用，这可能是由优化偏好和PLMS高冗余引起的。我们的研究结果表明，具有知识转移的改善PT是可能的并且有希望的，而提示的交叉任务转移性通常比跨模型转移性更好。

We study the problem of retrieval with instructions, where users of a retrieval system explicitly describe their intent along with their queries. We aim to develop a general-purpose task-aware retrieval system using multi-task instruction tuning, which can follow human-written instructions to find the best documents for a given query. We introduce the first large-scale collection of approximately 40 retrieval datasets with instructions, BERRI, and present TART, a multi-task retrieval system trained on BERRI with instructions. TART shows strong capabilities to adapt to a new retrieval task via instructions and advances the state of the art on two zero-shot retrieval benchmarks, BEIR and LOTTE, outperforming models up to three times larger. We further introduce a new evaluation setup, X^2-Retrieval to better reflect real-world scenarios, where diverse domains and tasks are pooled and a system needs to find documents aligning users' intents. In this setup, TART significantly outperforms competitive baselines, further demonstrating the effectiveness of guiding retrieval with instructions.

微调下游任务的大型预训练语言模型已成为NLP中的事实上学习范式。然而，常规方法微调预先训练模型的所有参数，这变得越来越稳定，因为模型尺寸和增长的任务数量。最近的工作提出了各种参数有效的转移学习方法，只需微调少数（额外）参数以获得强大的性能。虽然有效，但各种方法中的成功和联系的关键成分尚不清楚。在本文中，我们分解了最先进的参数有效的传输学习方法的设计，并提出了一个在它们之间建立连接的统一框架。具体而言，我们将它们重新框架作为预先训练的模型对特定隐藏状态的修改，并定义了一组设计尺寸，不同的方法变化，例如计算修改的功能和应用修改的位置。通过跨机翻译的全面实证研究，文本摘要，语言理解和文本分类基准，我们利用统一的视图来确定以前的方法中的重要设计选择。此外，我们的统一框架使得能够在不同的方法中传输设计元素，因此我们能够实例化新的参数高效的微调方法，该方法比以前的方法更加有效，而是更有效，实现可比的结果在所有四个任务上调整所有参数。

迅速调整，它冻结了预审计的语言模型（PLM），只有微调的几个额外软提示的参数，在PLM具有数十亿个参数时，对全参数微调（即模型调整）显示出具有竞争性的性能，但仍然显示出竞争力。在较小的PLM的情况下，性能差。因此，迅速转移（POT），通过训练有素的类似源任务的提示来初始化目标提示，最近提议改善及时调整。但是，这样的香草锅方法通常会实现次优的性能，因为（i）锅对源目标对的相似性和（ii）直接对目标提示进行初始提示的提示敏感，而目标任务可能会导致灾难性忘记来源知识。为了解决这些问题，我们提出了一个新的指标，以准确预测及时的转移性（关于（i）），以及一种利用知识蒸馏技术将“知识”从源提示转移到的新颖的锅方法（即熊猫）目标以微妙的方式提示，并有效缓解灾难性遗忘（关于（ii））。此外，为了实现每个源目标对的自适应及时转移，我们使用指标来控制熊猫方法中的知识转移。对PLM的5个量表的21个源和9个目标数据集的189组组合进行了广泛而系统的实验，表明：1）我们提出的指标很好地预测了及时的可传递性； 2）在所有任务和型号中，我们的熊猫始终优于香草锅的平均得分2.3％（最高24.1％）； 3）通过我们的熊猫方法，及时调整可以比在各种PLM量表场景中的模型调整来实现竞争性甚至更好的性能。接受代码和模型将在接受后发布。

Query-focused summarization has been considered as an important extension for text summarization. It aims to generate a concise highlight for a given query. Different from text summarization, query-focused summarization has long been plagued by the problem of lacking high-quality large-scale datasets. In this paper, we investigate the idea that whether we can integrate and transfer the knowledge of text summarization and question answering to assist the few-shot learning in query-focused summarization. Here, we propose prefix-merging, a prefix-based pretraining strategy for few-shot learning in query-focused summarization. Drawn inspiration from prefix-tuning, we are allowed to integrate the task knowledge from text summarization and question answering into a properly designed prefix and apply the merged prefix to query-focused summarization. With only a small amount of trainable parameters, prefix-merging outperforms fine-tuning on query-focused summarization. We further discuss the influence of different prefix designs and propose a visualized explanation for how prefix-merging works.

Fine-tuning large pre-trained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new model is required for every task. As an alternative, we propose transfer with adapter modules. Adapter modules yield a compact and extensible model; they add only a few trainable parameters per task, and new tasks can be added without revisiting previous ones. The parameters of the original network remain fixed, yielding a high degree of parameter sharing. To demonstrate adapter's effectiveness, we transfer the recently proposed BERT Transformer model to 26 diverse text classification tasks, including the GLUE benchmark. Adapters attain near state-of-the-art performance, whilst adding only a few parameters per task. On GLUE, we attain within 0.4% of the performance of full fine-tuning, adding only 3.6% parameters per task. By contrast, fine-tuning trains 100% of the parameters per task.