Recent work has shown that large language models are capable of generating natural language reasoning steps or Chains-of-Thoughts (CoT) to answer a multi-step question when prompted to do so. This is insufficient, however, when the necessary knowledge is not available or up-to-date within a model's parameters. A straightforward approach to address this is to retrieve text from an external knowledge source using the question as a query and prepend it as context to the model's input. This, however, is also insufficient for multi-step QA where \textit{what to retrieve} depends on \textit{what has already been derived}. To address this issue we propose IRCoT, a new approach that interleaves retrieval with CoT for multi-step QA, guiding the retrieval with CoT and in turn using retrieved results to improve CoT. Our experiments with GPT3 show substantial improvements in retrieval (up to 22 points) and downstream QA (up to 16 points) over the baselines on four datasets: HotpotQA, 2WikiMultihopQA, MuSiQue, and IIRC. Notably, our method also works well for much smaller models such as T5-Flan-large (0.7B) without any additional training.

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

Question-answering datasets require a broad set of reasoning skills. We show how to use question decompositions to teach language models these broad reasoning skills in a robust fashion. Specifically, we use widely available QDMR representations to programmatically create hard-to-cheat synthetic contexts for real questions in six multi-step reasoning datasets. These contexts are carefully designed to avoid reasoning shortcuts prevalent in real contexts that prevent models from learning the right skills. This results in a pretraining dataset, named TeaBReaC, containing 525K multi-step questions (with associated formal programs) covering about 900 reasoning patterns. We show that pretraining standard language models (LMs) on TeaBReaC before fine-tuning them on target datasets improves their performance by up to 13 F1 points across 4 multi-step QA datasets, with up to 21 point gain on more complex questions. The resulting models also demonstrate higher robustness, with a 5-8 F1 point improvement on two contrast sets. Furthermore, TeaBReaC pretraining substantially improves model performance and robustness even when starting with numerate LMs pretrained using recent methods (e.g., PReasM, POET). Our work thus shows how to effectively use decomposition-guided contexts to robustly teach multi-step reasoning.

Fine Tuning Target Tasks的连续提示最近被出现为完整模型微调的紧凑替代方案。这些有前途的结果的动机，我们调查了提取离散（文本）解释的可行性，持续提示忠于他们解决的问题。在实践中，我们在通过连续提示和最近的邻离分立投影解决的任务之间的“任性”行为：我们可以找到解决任务的连续提示，同时投射到任意文本（例如，不同甚至a的定义矛盾的任务），而在最佳连续提示的非常小（2％）的边缘内，对于任务相同的相同尺寸。我们提供这种奇怪和令人惊讶的行为背后的直觉，以及广泛的实证分析量化各种参数的效果。例如，对于更大的模型大小，我们观察到更高的任性，即，我们可以发现提示更紧密地映射到任何随意的任意文本，精度较小。这些调查结果与忠实地解释模型和任务持续提示及其概括的难度有关的重要意义，为提示语言模型的未来进展提供指导。

Generative models learned from training using deep learning methods can be used as priors in inverse under-determined inverse problems, including imaging from sparse set of measurements. In this paper, we present a novel hierarchical deep-generative model MrSARP for SAR imagery that can synthesize SAR images of a target at different resolutions jointly. MrSARP is trained in conjunction with a critic that scores multi resolution images jointly to decide if they are realistic images of a target at different resolutions. We show how this deep generative model can be used to retrieve the high spatial resolution image from low resolution images of the same target. The cost function of the generator is modified to improve its capability to retrieve the input parameters for a given set of resolution images. We evaluate the model's performance using the three standard error metrics used for evaluating super-resolution performance on simulated data and compare it to upsampling and sparsity based image sharpening approaches.

Modern Deep Learning (DL) models have grown to sizes requiring massive clusters of specialized, high-end nodes to train. Designing such clusters to maximize both performance and utilization to amortize their steep cost is a challenging task requiring careful balance of compute, memory, and network resources. Moreover, a plethora of each model's tuning knobs drastically affect the performance, with optimal values often depending on the underlying cluster's characteristics, which necessitates a complex cluster-workload co-design process. To facilitate the design space exploration of such massive DL training clusters, we introduce COMET a holistic cluster design methodology and workflow to jointly study the impact of parallelization strategies and key cluster resource provisioning on the performance of distributed DL training. We develop a step-by-step process to establish a reusable and flexible methodology, and demonstrate its application with a case study of training a Transformer-1T model on a cluster of variable compute, memory, and network resources. Our case study demonstrates COMET's utility in identifying promising architectural optimization directions and guiding system designers in configuring key model and cluster parameters.

Recent developments in the methods of explainable AI (XAI) methods allow researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial information about input-output relationships and realizing how data connects with machine learning models. In this paper we explore interpretability of DNN models designed to identify jets coming from top quark decay in high energy proton-proton collisions at the Large Hadron Collider (LHC). We review a subset of existing top tagger models and explore different quantitative methods to identify which features play the most important roles in identifying the top jets. We also investigate how and why feature importance varies across different XAI metrics, how feature correlations impact their explainability, and how latent space representations encode information as well as correlate with physically meaningful quantities. Our studies uncover some major pitfalls of existing XAI methods and illustrate how they can be overcome to obtain consistent and meaningful interpretation of these models. We additionally illustrate the activity of hidden layers as Neural Activation Pattern (NAP) diagrams and demonstrate how they can be used to understand how DNNs relay information across the layers and how this understanding can help to make such models significantly simpler by allowing effective model reoptimization and hyperparameter tuning. By incorporating observations from the interpretability studies, we obtain state-of-the-art top tagging performance from augmented implementation of existing network

多种业务场景需要从结构化输入数据中自动生成描述性的人类可读文本。因此，已经开发了针对各种下游任务的事实到文本的系统主要是由于相关数据集的高可用性。直到最近，提出了跨语言事实与文本（XF2T）的问题，该问题是针对多种语言的生成，以及一个数据集，Xalign的八种语言。但是，实际上XF2T生成问题没有严格的工作。我们使用另外四种语言的注释数据扩展了Xalign数据集：旁遮普语，马拉雅拉姆语，阿萨姆语和Oriya。我们在扩展的多语言数据集上使用基于变压器的流行文本生成模型进行了广泛的研究，我们称之为Xalignv2。此外，我们研究了不同文本生成策略的性能：预处理，事实感知的嵌入和结构意识的输入编码的多种变化。我们的广泛实验表明，使用具有结构意识的输入编码的事实感知的嵌入式的多语言MT5模型可以平均在十二种语言中获得最佳结果。我们将代码，数据集和模型公开可用，并希望这将有助于进一步在此关键领域进行进一步的研究。

我们为由随机微分方程（SDE）控制的物理系统提出了一种新型的灰色盒建模算法。所提出的方法（称为深物理校正器（DPC））将用SDE代表的物理学与深神经网络（DNN）相结合。这里的主要思想是利用DNN来建模缺失的物理学。我们假设将不完整的物理与数据相结合将使模型可解释并允许更好地概括。与随机模拟器的训练替代模型相关的主要瓶颈通常与选择合适的损耗函数有关。在文献中可用的不同损失函数中，我们在DPC中使用有条件的最大平均差异（CMMD）损失函数，因为其证明了其性能。总体而言，物理数据融合和CMMD允许DPC从稀疏数据中学习。我们说明了拟议的DPC在文献中的四个基准示例上的性能。获得的结果高度准确，表明它可能将其作为随机模拟器的替代模型的应用。

一种独立的自动解码和转录口服语音方法称为自动语音识别（ASR）。典型的ASR系统提取物从音频录制或流中列出，并运行一种或多种算法以将功能映射到相应的文本。近年来，在语音信号处理领域进行了许多研究。当获得足够的资源时，常规的ASR和新兴的端到端（E2E）语音识别都产生了有希望的结果。但是，对于像孟加拉这样的低资源语言，ASR的当前状态落后于落后，尽管低资源状态并没有反映出这一语言是全世界有超过5亿人使用的。尽管它很受欢迎，但并没有很多可用的开源数据集，因此很难对孟加拉语语音识别系统进行研究。本文是名为“ Buet CSE Fest DL Sprint”的比赛的一部分。本文的目的是通过基于转移学习框架在E2E结构上采用语音识别技术来提高孟加拉语的语音识别表现。提出的方法有效地对孟加拉语语言进行了建模，并在7747个样本的测试数据集上以“ Levenshtein平均距离”获得3.819分数，而仅使用1000个火车数据集样本进行培训。