参数效率的方法能够使用单个冷冻的预训练的大语言模型（LLM）来通过学习特定于任务的软提示来执行许多任务，从而在串联到输入文本时调节模型行为。但是，这些学习的提示与给定的冷冻模型紧密耦合 - 如果模型已更新，则需要获得相应的新提示。在这项工作中，我们提出并调查了几种“提示回收”的方法，其中将在源模型上进行了及时培训以与新目标模型一起使用。我们的方法不依赖于目标模型的有监督的提示，特定于任务的数据或培训更新，这与从头开始的目标模型重新调整提示一样昂贵。我们表明，模型之间的回收是可能的（我们的最佳设置能够成功回收$ 88.9 \％的提示，从而产生一个提示，即表现出色的基线），但是剩下的大量性能净空，需要改进的回收技术。

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.

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.

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.

在这项工作中，我们证明了多种语的大规模序列到序列（SEQ2SEQ）模型，该模型是通过Denoising和因果语言建模（CLM）任务的混合物进行训练的，比仅解码器模型更有效地进行了效率的学习者在各种任务上。特别是，我们培训了一个名为Alexa教师模型（Alexatm 20b）的200亿个参数多语言SEQ2SEQ模型，并表明它在1-Shot摘要任务上实现了最先进的（SOTA）性能，超过了更大的540B PALM DOPODER模型。 Alexatm 20b还可以在1-Shot Machine翻译中实现SOTA，尤其是对于低资源语言，几乎所有语言对（阿拉伯语，英语，法语，德语，德语，印地语，意大利语，日语，以及flores-101数据集上的泰卢固语）。我们还显示了零拍设置，AlexATM 20B在SuperGlue和SqueadV2数据集上的表现优于GPT3（175B），并在XNLI，XCOPA，PAWS-X和XWINOGRAD等多语言任务上提供SOTA性能。总体而言，我们的结果为SEQ2SEQ模型提供了一个令人信服的案例，作为大型语言模型（LLM）培训的仅解码器模型的强大替代方法。

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。

Traditional multi-task learning architectures train a single model across multiple tasks through a shared encoder followed by task-specific decoders. Learning these models often requires specialized training algorithms that address task-conflict in the shared parameter updates, which otherwise can lead to negative transfer. A new type of multi-task learning within NLP homogenizes multi-task architectures as a shared encoder and language model decoder, which does surprisingly well across a range of diverse tasks. Does this new architecture suffer from task-conflicts that require specialized training algorithms? We study how certain factors in the shift towards text-to-text models affects multi-task conflict and negative transfer, finding that both directional conflict and transfer are surprisingly constant across architectures.

With increasing scale, large language models demonstrate both quantitative improvement and new qualitative capabilities, especially as zero-shot learners, like GPT-3. However, these results rely heavily on delicate prompt design and large computation. In this work, we explore whether the strong zero-shot ability could be achieved at a smaller model scale without any external supervised data. To achieve this goal, we revisit masked language modeling and present a geometry-guided self-supervised learning method (Go-tuningfor short) by taking a small number of task-aware self-supervised data to update language models further. Experiments show that Go-tuning can enable T5-small (80M) competitive zero-shot results compared with large language models, such as T5-XL (3B). We also apply Go-tuning on multi-task settings and develop a multi-task model, mgo-T5 (250M). It can reach the average performance of OPT (175B) on 9 datasets.

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。

petroni等。 （2019）证明，可以通过将它们表达为冻结式提示并将模型的预测准确性解释为下限，作为其编码的事实信息量的较低限制，从预先接收的语言模型中检索世界事实。随后的工作已经尝试通过搜索更好的提示来缩回估计，使用不相交的事实作为培训数据。在这项工作中，我们制作两个互补贡献，以更好地了解这些事实探测技术。首先，我们提出了OptiPrompt，一种新颖的和有效的方法，直接在连续嵌入空间中优化。我们发现这种简单的方法能够预测喇嘛基准中的额外6.4％的事实。其次，我们提出了一个更重要的问题：我们真的可以将这些探测结果解释为下限吗？这些提示搜索方法是否有可能从培训数据中学习？我们发现，有些令人惊讶的是，这些方法使用的培训数据包含了潜在的事实分布的某些规则，以及所有现有的提示方法，包括我们的方法，可以利用它们以获得更好的事实预测。我们开展一系列控制实验来解除“学习”从“学习召回”，提供了更详细的图片，不同的提示可以揭示关于预先接受的语言模型。

Language models can be prompted to perform a wide variety of zero- and few-shot learning problems. However, performance varies significantly with the choice of prompt, and we do not yet understand why this happens or how to pick the best prompts. In this work, we analyze the factors that contribute to this variance and establish a new empirical hypothesis: the performance of a prompt is coupled with the extent to which the model is familiar with the language it contains. Over a wide range of tasks, we show that the lower the perplexity of the prompt is, the better the prompt is able to perform the task. As a result, we devise a method for creating prompts: (1) automatically extend a small seed set of manually written prompts by paraphrasing using GPT3 and backtranslation and (2) choose the lowest perplexity prompts to get significant gains in performance.

Existing techniques for training language models can be misaligned with the truth: if we train models with imitation learning, they may reproduce errors that humans make; if we train them to generate text that humans rate highly, they may output errors that human evaluators can't detect. We propose circumventing this issue by directly finding latent knowledge inside the internal activations of a language model in a purely unsupervised way. Specifically, we introduce a method for accurately answering yes-no questions given only unlabeled model activations. It works by finding a direction in activation space that satisfies logical consistency properties, such as that a statement and its negation have opposite truth values. We show that despite using no supervision and no model outputs, our method can recover diverse knowledge represented in large language models: across 6 models and 10 question-answering datasets, it outperforms zero-shot accuracy by 4\% on average. We also find that it cuts prompt sensitivity in half and continues to maintain high accuracy even when models are prompted to generate incorrect answers. Our results provide an initial step toward discovering what language models know, distinct from what they say, even when we don't have access to explicit ground truth labels.

GPT-3等大型自回归语言模型是几秒钟的学习者，可以在没有微调的情况下执行各种语言任务。虽然已知这些模型能够共同代表许多不同的语言，但他们的培训数据由英语主导，可能限制了它们的交叉概括。在这项工作中，我们在覆盖多种语言的平衡语料库上培训多语言自回归语言模型，并在广泛的任务中研究他们几乎没有零点的学习能力。我们最大的模型，具有75亿参数，在20多种代表语言中，在几种代表语言中，在几种代表性语言中，在几种代表性语言中，在多语言型号推理中表现出可比大小的GPT-3（在0次设置和0次拍摄设置中的绝对精度改善+ 7.4％ 4-拍摄设置中的9.4％）和自然语言推理（每次拍摄和4次设置中的每一个+ 5.4％）。在Flores-101机器翻译基准测试中，我们的模型优于GPT-3在182个翻译方向上有32个培训例子，同时超过45个方向的官方监督基线。我们介绍了模型成功和失败的位置的详细分析，特别是它尤其显示在某些任务中实现交叉语境的内容学习，而仍然存在改善表面的鲁棒性和适应没有a的任务的余地自然冻结形式。最后，我们评估我们在仇恨语音检测中以五种语言的仇恨语音检测的模型，并发现它具有与可比大小的GPT-3模型类似的限制。

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。

Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation). For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PALM 540B by a large margin (+9.4% on average). Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints, which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.

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。

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.

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。

通过自我监督的学习预先训练的大型语言模型在各种各样的任务上表现出令人印象深刻的零击功能。在这项工作中，我们介绍了Welm：一种针对中文的精心读取的预训练的语言模型，能够无缝执行不同类型的任务，以零或几次演示。 Welm通过“阅读”涵盖广泛主题的精选高质量语料库来接受10b参数的培训。我们表明，韦尔姆拥有有关各种领域和语言的广泛知识。在18个单语（中文）任务中，WELM可以大大优于现有的预训练模型，尺寸相似，并匹配高达25倍大的模型的性能。韦尔姆还表现出强大的多种语言和代码转换理解的能力，优于预先对30种语言进行预培训的现有多语言模型。此外，我们收集了人工编写的提示，并通过多次培训进行了大量的中文和微调韦尔姆的监督数据集。最终的模型可以实现对看不见的任务类型的强烈概括，并在零射门学习中优于无监督的韦尔姆。最后，我们证明韦尔姆具有解释和校准自己的决策的基本技能，这可能是未来研究的有希望的方向。我们的模型可以从https://welm.weixin.qq.com/docs/api/应用。

Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few-shot generalization to unseen tasks. However, there is a limited understanding of the performance trade-offs of different decisions made during the instruction-tuning process. These decisions include the scale and diversity of the instruction-tuning benchmark, different task sampling strategies, fine-tuning with and without demonstrations, training using specialized datasets for reasoning and dialogue, and finally, the fine-tuning objectives themselves. In this paper, we characterize the effect of instruction-tuning decisions on downstream task performance when scaling both model and benchmark sizes. To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks. Through the lens of this framework, we first present insights about instruction-tuning decisions as applied to OPT-30B and further exploit these insights to train OPT-IML 30B and 175B, which are instruction-tuned versions of OPT. OPT-IML demonstrates all three generalization abilities at both scales on four different evaluation benchmarks with diverse tasks and input formats -- PromptSource, FLAN, Super-NaturalInstructions, and UnifiedSKG. Not only does it significantly outperform OPT on all benchmarks but is also highly competitive with existing models fine-tuned on each specific benchmark. We release OPT-IML at both scales, together with the OPT-IML Bench evaluation framework.