Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge. This paper aims to understand LMs' strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments of 10 models and 4 augmentation methods on PopQA, our new open-domain QA dataset with 14k questions. We find that LMs struggle with less popular factual knowledge, and that scaling fails to appreciably improve memorization of factual knowledge in the tail. We then show that retrieval-augmented LMs largely outperform orders of magnitude larger LMs, while unassisted LMs remain competitive in questions about high-popularity entities. Based on those findings, we devise a simple, yet effective, method for powerful and efficient retrieval-augmented LMs, which retrieves non-parametric memories only when necessary. Experimental results show that this significantly improves models' performance while reducing the inference costs.

While the NLP community is generally aware of resource disparities among languages, we lack research that quantifies the extent and types of such disparity. Prior surveys estimating the availability of resources based on the number of datasets can be misleading as dataset quality varies: many datasets are automatically induced or translated from English data. To provide a more comprehensive picture of language resources, we examine the characteristics of 156 publicly available NLP datasets. We manually annotate how they are created, including input text and label sources and tools used to build them, and what they study, tasks they address and motivations for their creation. After quantifying the qualitative NLP resource gap across languages, we discuss how to improve data collection in low-resource languages. We survey language-proficient NLP researchers and crowd workers per language, finding that their estimated availability correlates with dataset availability. Through crowdsourcing experiments, we identify strategies for collecting high-quality multilingual data on the Mechanical Turk platform. We conclude by making macro and micro-level suggestions to the NLP community and individual researchers for future multilingual data development.

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.

我们介绍了Realtime QA，这是一个动态的问答（QA）平台，该平台宣布问题并定期评估系统（此版本每周）。实时质量检查询问当前世界，质量检查系统需要回答有关新事件或信息的问题。因此，它挑战了QA数据集中的静态，常规假设，并追求瞬时应用。我们在包括GPT-3和T5在内的大型语言模型上建立了强大的基线模型。我们的基准是一项持续的努力，该初步报告在过去一个月中提出了实时评估结果。我们的实验结果表明，GPT-3通常可以根据新的退休文档正确更新其生成结果，从而突出了最新信息检索的重要性。尽管如此，我们发现GPT-3倾向于在检索文件时返回过时的答案，这些文件没有提供足够的信息来找到答案。这表明了未来研究的重要途径：开放式域质量检查系统是否可以确定无法回答的案例，并与用户甚至检索模块进行通信以修改检索结果？我们希望实时质量检查能够刺激问题答案及其他问题的瞬时应用。

我们介绍了关于多语言信息访问（MIA）2022共享任务的研讨会的结果，评估了16种类型上多样性的语言中的跨语性开放回程答案（QA）系统。在此任务中，我们在14种类型上多样化的语言中调整了两个大规模的跨语性开放式质疑QA数据集，并使用了2种代表性不足的语言中的新注释的开放式QA数据：Tagalog和Tamil。四个团队提交了他们的系统。利用迭代开采的最佳系统是不同的负面示例和较大的预审慎模型达到32.2 F1，表现优于我们的基线4.5分。第二最佳系统使用实体感知的上下文化表示文档检索，并在泰米尔语（20.8 F1）方面取得了重大改进，而其他大多数系统的得分几乎为零。

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.

检索增强的代表在许多知识密集型的NLP任务中表现出最先进的表现，例如打开问题应答和事实验证。考虑到检索到的段落，这些模型训练以产生最终输出，这可能与原始查询无关，导致学习虚假线索或回答记忆。这项工作介绍了一种融入通道的证据性的方法 - 是否段落包含正确的证据来支持输出 - 培训发电机。我们介绍了一个多任务学习框架，共同生成最终输出并预测每个段落的证据性，利用新的任务不可行方法来获得{\ IT Silver}分证分性标签进行监督。我们在三个知识密集型任务中的五个数据集的实验表明，我们的新的证据引导发电机具有相同尺寸模型的直接对应的直接对应，并使Faviq-Ambig的最先进。我们将这些改进归因于辅助多任务学习和银证处分性挖掘技术。

象征性的AI社区越来越多地试图在神经符号结构中接受机器学习，但由于文化障碍，仍在挣扎。为了打破障碍，这份相当有思想的个人备忘录试图解释和纠正统计，机器学习和深入学习的惯例，从局外人的角度进行深入学习。它提供了一个分步协议，用于设计一个机器学习系统，该系统满足符号AI社区认真对待所必需的最低理论保证，即，它讨论“在哪些条件下，我们可以停止担心和接受统计机器学习。 “一些亮点：大多数教科书都是为计划专门研究STAT/ML/DL的人编写的，应该接受术语。该备忘录适用于经验丰富的象征研究人员，他们听到了很多嗡嗡声，但仍然不确定和持怀疑态度。有关STAT/ML/DL的信息目前太分散或嘈杂而无法投资。此备忘录优先考虑紧凑性，并特别注意与象征性范式相互共鸣的概念。我希望这份备忘录能节省时间。它优先考虑一般数学建模，并且不讨论任何特定的函数近似器，例如神经网络（NNS），SVMS，决策树等。它可以对校正开放。将此备忘录视为与博客文章相似的内容，采用有关Arxiv的论文的形式。

最近，受到许多领域的成功应用程序的启发，深度学习（DL）的CSI获取技术已获得了学术界和行业的大量研究兴趣。考虑到第五代（5G）新无线电（NR）网络的实际反馈机制，我们提出了针对CSI（AI4CSI）的两个实施方案，基于DL的接收器和端到端设计。根据光谱效率（SE），反馈开销和计算复杂性，在5G NR网络中评估了提出的AI4CSI方案，并与遗产方案进行了比较。为了证明这些方案是否可以在现实生活中使用，在我们的研究中使用了基于建模的基于建模的通道数据和实际测量的通道。当仅将基于DL的CSI采集应用于接收器几乎没有空气接口影响时，它在适度的反馈开销水平下提供了大约25 \％的SE增益。在5G演变过程中，将其部署在当前的5G网络中是可行的。对于基于端到端DL的CSI增强功能，评估还证明了其在SE上的额外性能增长，与基于DL的接收器相比，为6％-26％，与传统CSI方案相比，其33％-58％ 。考虑到其对空气接口设计的巨大影响，它将是第六代（6G）网络的候选技术，其中可以使用人工智能设计的空气界面。

Nesterov的加速准牛顿（L）Naq方法已经显示出在几个神经网络（NN）应用中使用Nesterov的加速梯度加速了传统（L）BFGS准牛顿方法。然而，每个迭代的两个梯度的计算增加了计算成本。动量加速的准牛顿（MOQ）方法表明，Nesterov的加速梯度可以近似为过去梯度的线性组合。此摘要将MoQ近似扩展到有限的内存NAQ并评估函数近似问题的性能。