我们介绍了Sparrow，这是一个寻求信息的对话代理，与提示的语言模型基线相比，训练有素，更有帮助，正确和无害。我们使用从人类反馈中的强化学习来培训我们的模型，以帮助人类评估者判断代理人的行为。首先，为了使我们的代理人更有帮助和无害，我们将良好对话的要求分解为代理人应遵循的自然语言规则，并分别向评估者询问每个规则。我们证明，这种崩溃使我们能够收集对代理行为的更多针对性的人类判断，并允许更有效的规则条件奖励模型。其次，我们的代理商在收集对模型声明的偏好判决时提供了支持事实主张的来源的证据。对于事实问题，麻雀提供的证据支持了78％的时间。比基线比基线更享受麻雀，同时对人类的对抗性探测更具弹性，在探测时只有8％的时间违反了我们的规则。最后，我们进行了广泛的分析，表明尽管我们的模型学会遵守我们的规则，但它可以表现出分布偏见。

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。

大多数现实世界的应用需要处理传感器噪声或预测性不确定性等旋能性，其中正式规格的所需行为是固有的概率。尽管正式核查确保神经网络的可靠性，但概率规格方向的进展受到限制。在这个方向上，我们首先介绍神经网络的概率规范的一般性，它捕获了概率网络（例如，贝叶斯神经网络，MC-Dropout Networks）和不确定输入（通过传感器噪声或其他扰动而产生的输入）。然后，我们提出了一种通过概括拉格朗日二元性的概念来验证这些规范的一般技术，替换具有“功能乘法器”的标准拉格朗日乘法器，其可以是给定层上激活的任意功能。我们表明，功能乘法器的最佳选择导致精确的验证（即，声音和完全验证），以及特定形式的乘法器，我们开发了易诊的实际验证算法。我们通过将它们应用于贝叶斯神经网络（BNNS）和MC差动网络，以及认证属性，以及诸如对分发超出（OOD）数据的抗逆性鲁棒性和鲁棒检测的认证性能来验证我们的算法。在这些任务中，与现有工作相比，我们能够提供明显更强烈的保证 - 例如，对于在CiFar-10上培训的VGG-64 MC-Tropout CNN，我们改进了认证的AUC（真实AUC的验证的下限）对于鲁棒的OOD检测（在CIFAR-100上）起价$ 0 \％\ lightarrow 29 \％$。同样，对于在MNIST培训的BNN，我们从60.2美元\％\ lightarrow 74.6 \％$提高了强大的准确性。此外，在一种新颖的规范 - 分布稳健的检测 - 我们从5 \％\ lightarrow 23 \％$的5 \％$。

We present, Naamapadam, the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. In each language, it contains more than 400k sentences annotated with a total of at least 100k entities from three standard entity categories (Person, Location and Organization) for 9 out of the 11 languages. The training dataset has been automatically created from the Samanantar parallel corpus by projecting automatically tagged entities from an English sentence to the corresponding Indian language sentence. We also create manually annotated testsets for 8 languages containing approximately 1000 sentences per language. We demonstrate the utility of the obtained dataset on existing testsets and the Naamapadam-test data for 8 Indic languages. We also release IndicNER, a multilingual mBERT model fine-tuned on the Naamapadam training set. IndicNER achieves the best F1 on the Naamapadam-test set compared to an mBERT model fine-tuned on existing datasets. IndicNER achieves an F1 score of more than 80 for 7 out of 11 Indic languages. The dataset and models are available under open-source licenses at https://ai4bharat.iitm.ac.in/naamapadam.

Deep learning models operating in the complex domain are used due to their rich representation capacity. However, most of these models are either restricted to the first quadrant of the complex plane or project the complex-valued data into the real domain, causing a loss of information. This paper proposes that operating entirely in the complex domain increases the overall performance of complex-valued models. A novel, fully complex-valued learning scheme is proposed to train a Fully Complex-valued Convolutional Neural Network (FC-CNN) using a newly proposed complex-valued loss function and training strategy. Benchmarked on CIFAR-10, SVHN, and CIFAR-100, FC-CNN has a 4-10% gain compared to its real-valued counterpart, maintaining the model complexity. With fewer parameters, it achieves comparable performance to state-of-the-art complex-valued models on CIFAR-10 and SVHN. For the CIFAR-100 dataset, it achieves state-of-the-art performance with 25% fewer parameters. FC-CNN shows better training efficiency and much faster convergence than all the other models.

Building segmentation in high-resolution InSAR images is a challenging task that can be useful for large-scale surveillance. Although complex-valued deep learning networks perform better than their real-valued counterparts for complex-valued SAR data, phase information is not retained throughout the network, which causes a loss of information. This paper proposes a Fully Complex-valued, Fully Convolutional Multi-feature Fusion Network(FC2MFN) for building semantic segmentation on InSAR images using a novel, fully complex-valued learning scheme. The network learns multi-scale features, performs multi-feature fusion, and has a complex-valued output. For the particularity of complex-valued InSAR data, a new complex-valued pooling layer is proposed that compares complex numbers considering their magnitude and phase. This helps the network retain the phase information even through the pooling layer. Experimental results on the simulated InSAR dataset show that FC2MFN achieves better results compared to other state-of-the-art methods in terms of segmentation performance and model complexity.

Object detection and classification using aerial images is a challenging task as the information regarding targets are not abundant. Synthetic Aperture Radar(SAR) images can be used for Automatic Target Recognition(ATR) systems as it can operate in all-weather conditions and in low light settings. But, SAR images contain salt and pepper noise(speckle noise) that cause hindrance for the deep learning models to extract meaningful features. Using just aerial view Electro-optical(EO) images for ATR systems may also not result in high accuracy as these images are of low resolution and also do not provide ample information in extreme weather conditions. Therefore, information from multiple sensors can be used to enhance the performance of Automatic Target Recognition(ATR) systems. In this paper, we explore a methodology to use both EO and SAR sensor information to effectively improve the performance of the ATR systems by handling the shortcomings of each of the sensors. A novel Multi-Modal Domain Fusion(MDF) network is proposed to learn the domain invariant features from multi-modal data and use it to accurately classify the aerial view objects. The proposed MDF network achieves top-10 performance in the Track-1 with an accuracy of 25.3 % and top-5 performance in Track-2 with an accuracy of 34.26 % in the test phase on the PBVS MAVOC Challenge dataset [18].

In this work, we introduce IndicXTREME, a benchmark consisting of nine diverse tasks covering 18 languages from the Indic sub-continent belonging to four different families. Across languages and tasks, IndicXTREME contains a total of 103 evaluation sets, of which 51 are new contributions to the literature. To maintain high quality, we only use human annotators to curate or translate\footnote{for IndicXParaphrase, where an automatic translation system is used, a second human verification and correction step is done.} our datasets. To the best of our knowledge, this is the first effort toward creating a standard benchmark for Indic languages that aims to test the zero-shot capabilities of pretrained language models. We also release IndicCorp v2, an updated and much larger version of IndicCorp that contains 20.9 billion tokens in 24 languages. We pretrain IndicBERT v2 on IndicCorp v2 and evaluate it on IndicXTREME to show that it outperforms existing multilingual language models such as XLM-R and MuRIL.

端到端（E2E）模型已成为最新语音识别系统的默认选择。此类型号经过大量标记数据的培训，这些数据通常无法用于低资源语言。诸如自我监督学习和转移学习的诺言之类的技术尚未在培训准确的模型中有效。另一方面，在各种域和扬声器集合上收集标记的数据集非常昂贵。在这项工作中，我们通过公共资料中的印度语言，特别是来自印度广播电台的公共档案馆的印度语言的``采矿''文本和音频对展示了这些方法的廉价和有效替代方案。作为关键组件，我们将Needleman-Wunsch算法调整为与相应的音频片段对齐句子，并给定长音频和其转录本的PDF，同时由于OCR，无关紧要的文本和未转录的语音而对错误进行了强大的态度。因此，我们创建了Shrutilipi，这是一个数据集，其中包含超过6,400个小时的12个印度语言标签的音频，总计为495万个句子。平均而言，Shrutilipi导致2.3倍增加了公开可用的标签数据。我们在12种语言中与21种人类评估者建立了Shrutilipi的质量。我们还根据代表区域，说话者和提到的实体建立了Shrutilipi的多样性。值得注意的是，我们表明，将Shrutilipi添加到WAV2VEC模型的训练集中，导致在Indicsuperb基准上的7种语言中，平均降低了5.8 \％。对于具有最多基准的印地语（7），平均水平从18.8％下降到13.5％。这种改进扩展到有效的模型：对于构象异构体模型（比WAV2VEC小10倍），我们显示出2.3％的下降。最后，我们通过证明对其进行训练的模型对嘈杂的输入更强大，证明了Shrutilipi的多样性。

卷积神经网络（CNN）在许多计算机视觉应用中表现出非常吸引人的性能。通常使用基于随机梯度下降（SGD）优化技术进行CNN的训练。基于自适应动量的SGD优化器是最近的趋势。但是，现有的优化器无法在一阶时刻保持零平均值，并在优化方面挣扎。在本文中，我们提出了针对CNN的基于集中化的SGD优化器。具体而言，我们明确地将零均值约束强加于一阶力矩。提出的力矩集中化本质上是通用的，可以与任何现有的自适应动量优化器集成。提出的想法通过三种最先进的优化技术进行了测试，包括基准CIFAR10，CIFAR100和TINYIMAGENET数据集的ADAM，RADAM和ADABELIEF，用于图像分类。与建议的力矩集中化集成时，现有优化器的性能通常会提高。此外，提议的力矩集中化的结果也比现有的梯度集中化更好。使用玩具示例的分析分析表明，所提出的方法导致较短，更平滑的优化轨迹。源代码可在\ url {https://github.com/sumanthsadhu/mc-optimizer}中公开获得。