We propose eXtensible Prompt (X-Prompt) for prompting a large language model (LLM) beyond natural language (NL). X-Prompt instructs an LLM with not only NL but also an extensible vocabulary of imaginary words that are introduced to help represent what NL words hardly describe, allowing a prompt to be more descriptive. Like NL prompts, X-Prompt is out-of-distribution (OOD) robust, for which we propose context-guided learning with prompt augmentation to learn its imaginary words for general usability, enabling them to use in different prompt contexts for fine-grain specifications. The promising results of X-Prompt demonstrate its potential of approaching advanced interaction between humans and LLMs to bridge their communication gap.

Task agnostic generative pretraining (GPT) has recently proved promising for zero- and few-shot learning, gradually diverting attention from the expensive supervised learning paradigm. Although the community is accumulating knowledge as to capabilities of English-language autoregressive models such as GPT-3 adopting this generative approach, scholarship about these models remains acutely Anglocentric. Consequently, the community currently has serious gaps in its understanding of this class of models, their potential, and their societal impacts in diverse settings, linguistic traditions, and cultures. To alleviate this issue for Arabic, a collection of diverse languages and language varieties with more than $400$ million population, we introduce JASMINE, a suite of powerful Arabic autoregressive Transformer language models ranging in size between 300 million-13 billion parameters. We pretrain our new models with large amounts of diverse data (400GB of text) from different Arabic varieties and domains. We evaluate JASMINE extensively in both intrinsic and extrinsic settings, using a comprehensive benchmark for zero- and few-shot learning across a wide range of NLP tasks. We also carefully develop and release a novel benchmark for both automated and human evaluation of Arabic autoregressive models focused at investigating potential social biases, harms, and toxicity in these models. We aim to responsibly release our models with interested researchers, along with code for experimenting with them

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。

GPT-3显示了培训的大规模语言模型（LMS）的卓越情调学习能力，培训数十亿规模数据。在这里，我们解决了GPT-3纸张报告的一些剩余问题，例如非英语LM，不同大小模型的性能，以及最近引入的迅速优化对上下文学习的效果。为实现这一目标，我们介绍了HyperClova，一个韩国VPT-3的韩国变体训练在一个以韩国为中心的560b标准的令牌。通过我们的韩国特定标记化，HyperClova与我们的培训配置增强，显示了韩国各种下游任务的最先进的上下游零射击和几秒钟学习表演。此外，我们展示了基于及时的学习的性能优势，并演示如何集成到迅速的工程管道中。然后，我们讨论了通过引入Hyperclova Studio，互动提示工程界面向ML的非专家提供AI原型设计能力来实现No Code AI范例的可能性。最后，我们展示了我们具有三个成功的内部应用程序的方法的潜力。

Large pretrained language models generate fluent text but are notoriously hard to controllably sample from. In this work, we study constrained sampling from such language models: generating text that satisfies user-defined constraints, while maintaining fluency and the model's performance in a downstream task. We propose MuCoLa -- a sampling procedure that combines the log-likelihood of the language model with arbitrary (differentiable) constraints in a single energy function, and then generates samples in a non-autoregressive manner. Specifically, it initializes the entire output sequence with noise and follows a Markov chain defined by Langevin Dynamics using the gradients of the energy function. We evaluate MuCoLa on text generation with soft and hard constraints as well as their combinations obtaining significant improvements over competitive baselines for toxicity avoidance, sentiment control, and keyword-guided generation.

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.

预处理的基于变压器的语言模型（LMS）显示出显着的自然语言生成能力。凭借其巨大的潜力，控制这种LM的文本生成引起了人们的关注。尽管有一些研究试图控制生成的文本的高级属性（例如情感和主题），但仍然缺乏对其在单词和短语级别上的内容的更精确的控制。在这里，我们建议内容调节器（COCON）以细粒度的水平控制LM的输出文本。在我们的自我监督方法中，Cocon Block学会了通过调节从LM中扣留的内容输入来帮助LM完成部分观察到的文本序列。通过实验，我们表明Cocon可以自然地将目标内容纳入生成的文本中，并以零拍的方式控制高级文本属性。

深层生成模型有可能从根本上改变我们创建高保真数字内容的方式，但通常很难控制。提示生成模型是一个有希望的最新发展，原则上，最终用户可以创造性地利用零击和几乎没有学习的学习来将新任务分配给AI Ad-Hoc，只需将其写下即可。但是，对于大多数最终用户而言，编写有效提示目前主要是试验和错误过程。为了解决这个问题，我们讨论了使用促使人类互动的新范式的交互式创意应用程序的关键机会和挑战。根据我们的分析，我们为支持提示的用户界面提出了四个设计目标。我们用混凝土UI设计草图说明了这些内容，重点是创意写作的用例。HCI和AI的研究社区可以将这些作为起点，以开发足够的用户界面，以供能够零和少数学习的模型。

在本文中，我们利用大型语言模型（LMS）来执行零拍文本样式传输。我们介绍了一个提示方法，我们称之为零射击学习，框架样式传输作为句子重写任务，并且只需要一种自然语言指令，而无需在目标样式中的模型微调或示例。增强零射击学习很简单，并展示了不仅仅是关于诸如情感等标准的转移任务的有前途的结果，还可以在“使这种丝身态”或“插入隐喻”等任意变换上。

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.

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.

现在，可以使用最先进的神经语言模型通过零射门提示来解决临时语言任务，而无需进行监督培训。近年来，这种方法已广受欢迎，研究人员证明了提示在特定的NLP任务上实现强烈准确的提示。但是，找到新任务的提示需要实验。具有不同措辞选择的不同提示模板会导致明显的准确性差异。提示允许用户尝试及时变化，可视化及时性能，并迭代优化提示。我们开发了一个工作流程，该工作流程允许用户首先使用少量数据专注于模型反馈，然后再进入大型数据制度，该数据制度允许使用任务的定量度量来实现有希望的提示的经验基础。然后，该工具可以轻松部署新创建的临时模型。我们使用多种现实世界用例演示了Fackide（http://prompt.vizhub.ai）和我们的工作流程的实用性。

As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90-100% of labels, sometimes more so than corresponding human-written datasets. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user's preferred answer ("sycophancy") and express greater desire to pursue concerning goals like resource acquisition and goal preservation. We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down. Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors.

诸如Openai的GPT-3等复杂的语言模型可以生成针对边缘化群体的可恶文本。鉴于此容量，我们有兴趣是否可以使用大型语言模型来识别仇恨言论，并将文本分类为性别歧视或种族主义？我们使用GPT-3识别性别歧视和种族主义文本段落，具有零，单次和几秒钟。我们发现，通过零射门和一拍学习，GPT-3可以识别性别歧视或种族主义文本，精度为48％和69％。随着少量学习和提示中的指令，模型的准确性可以高达78％。我们得出结论，大型语言模型在仇恨语音检测中发挥作用，并且具有进一步的开发语言模型可以用来抵制仇恨言论甚至自我警察。

最近已被证明大型语言模型在各种任务集中获得合理的零射普通化（Brown等，2020）。它已经假设这是语言模型的隐式多任务学习的结果，在语言模型中的预押（Radford等，2019）。可以通过明确的多任务学习直接引起零拍常规化？为了以缩放测试这个问题，我们开发一个系统，以便轻松地将任何自然语言任务映射到人类可读的提示表单中。我们转换一组大量的监督数据集，每个数据集都有多个提示，具有不同的措辞。这些提示的数据集允许基准测试模型执行完全看不见的任务的能力。我们介绍了一个普拉克尔编码器 - 解码器模型（Raffel等，2020; Lester等，2021），覆盖各种任务。该模型在多个标准数据集中达到强大的零点性能，通常优于其尺寸的型号超过16倍。此外，我们的方法对来自Big-替补基准测试的任务子集具有强烈性能，优于其尺寸的6倍。所有提示和培训的型号都可以在https://github.com/ bigscience-workshop / protectsource / httpsource / https：//huggingface.co/bigscience/t0pp。

Large language models can perform new tasks in a zero-shot fashion, given natural language prompts that specify the desired behavior. Such prompts are typically hand engineered, but can also be learned with gradient-based methods from labeled data. However, it is underexplored what factors make the prompts effective, especially when the prompts are natural language. In this paper, we investigate common attributes shared by effective prompts. We first propose a human readable prompt tuning method (F LUENT P ROMPT) based on Langevin dynamics that incorporates a fluency constraint to find a diverse distribution of effective and fluent prompts. Our analysis reveals that effective prompts are topically related to the task domain and calibrate the prior probability of label words. Based on these findings, we also propose a method for generating prompts using only unlabeled data, outperforming strong baselines by an average of 7.0% accuracy across three tasks.

大型语言模型会产生类似人类的文本，这些文本推动了越来越多的应用。但是，最近的文献以及越来越多的现实世界观察表明，这些模型可以产生有毒，有偏见，不真实或其他有害的语言。尽管正在进行评估语言模型危害的工作，但要远见卓识转换出可能出现的危害可能会引起严格的基准。为了促进这种翻译，我们概述了六种表征有害文本的方式，这些方法在设计新基准时值得明确考虑。然后，我们将这些特征用作镜头来识别现有基准中的趋势和差距。最后，我们将它们应用于视角API的案例研究，这是一种毒性分类器，被广泛用于HARS基准。我们的特征提供了一块桥梁，可以在远见和有效评估之间转化。

大型语言模型，例如OpenAI的法典和DeepMind的字母，可以生成代码来解决以自然语言表达的各种问题。这项技术已经在至少一项广泛使用的编程编辑器扩展程序中进行了商业化：Github Copilot。在本文中，我们探讨了具有大型语言模型（LLM辅助编程）的编程与程序员协助的先前概念化相似，并且与众不同。我们借鉴了公开可用的经验报告，有关LLM辅助编程以及先前的可用性和设计研究。我们发现，尽管LLM辅助编程通过搜索和重用分享了一些编译，配对编程和编程的属性，但技术可能性和实践经验都存在根本差异。因此，应该将LLM辅助编程视为具有自己独特的属性和挑战的新方法。最后，我们借鉴了用户研究的观察结果，在该观察中，非专家最终用户程序员使用LLM辅助工具来求解电子表格中的数据任务。我们讨论可能出现的问题，并在将大型语言模型应用于最终用户编程时，尤其是对于几乎没有编程专业知识的用户。

Many real-world applications of language models (LMs), such as code autocomplete and writing assistance, involve human-LM interaction, but the main LM benchmarks are non-interactive, where a system produces output without human intervention. To evaluate human-LM interaction, we develop a framework, Human-AI Language-based Interaction Evaluation (H-LINE), that expands non-interactive evaluation along three dimensions, capturing (i) the interactive process, not only the final output; (ii) the first-person subjective experience, not just a third-party assessment; and (iii) notions of preference beyond quality. We then design five tasks ranging from goal-oriented to open-ended to capture different forms of interaction. On four state-of-the-art LMs (three variants of OpenAI's GPT-3 and AI21's J1-Jumbo), we find that non-interactive performance does not always result in better human-LM interaction and that first-person and third-party metrics can diverge, suggesting the importance of examining the nuances of human-LM interaction.

语言模型（LMS）已被证明在各种下游应用程序中很有用，例如摘要，翻译，问答和文本分类。由于它们可以存储的大量信息，LMS正在成为人工智能中越来越重要的工具。在这项工作中，我们提出了道具（提示为探测），该道具利用GPT-3（最初由OpenAI在2020年提出的大型语言模型）来执行知识基础构建任务（KBC）。 Prop实施了一种多步骤方法，该方法结合了各种提示技术来实现这一目标。我们的结果表明，手动提示策划是必不可少的，必须鼓励LM给出可变长度的答案集，特别是包括空的答案集，True/False问题是提高LM生成的建议精度的有用设备。 LM的大小是至关重要的因素，并且实体字典别名提高了LM评分。我们的评估研究表明，这些提出的技术可以大大提高最终预测的质量：Prop赢得了LM-KBC竞争的轨道2，表现优于基线36.4个百分点。我们的实施可在https://github.com/hemile/iswc-challenge上获得。