Multilingual models are often particularly dependent on scaling to generalize to a growing number of languages. Compression techniques are widely relied upon to reconcile the growth in model size with real world resource constraints, but compression can have a disparate effect on model performance for low-resource languages. It is thus crucial to understand the trade-offs between scale, multilingualism, and compression. In this work, we propose an experimental framework to characterize the impact of sparsifying multilingual pre-trained language models during fine-tuning. Applying this framework to mBERT named entity recognition models across 40 languages, we find that compression confers several intriguing and previously unknown generalization properties. In contrast to prior findings, we find that compression may improve model robustness over dense models. We additionally observe that under certain sparsification regimes compression may aid, rather than disproportionately impact the performance of low-resource languages.

现代机器学习研究依赖于相对较少的精心策划数据集。即使在这些数据集中，通常在“不整合”或原始数据中，从业人员也面临着重要的数据质量和多样性问题，这些问题可能会非常强烈地解决。应对这些挑战的现有方法往往会对特定问题做出强烈的假设，并且通常需要先验知识或元数据，例如域标签。我们的工作与这些方法是正交的：相反，我们专注于为元数据考古学提供一个统一和有效的框架 - 在数据集中发现和推断示例的元数据。我们使用简单的转换策划了可能存在的数据集（例如，错误标记，非典型或过度分布示例）中可能存在的数据子集，并利用这些探针套件之间的学习动力学差异来推断感兴趣的元数据。我们的方法与跨不同任务的更复杂的缓解方法相提并论：识别和纠正标签错误的示例，对少数民族样本进行分类，优先考虑与培训相关的点并启用相关示例的可扩展人类审核。

从有限的资源中获得最大收益可以进步自然语言处理（NLP）研究和实践，同时保守资源。这些资源可能是数据，时间，存储或能源。NLP的最新工作从缩放率产生了有趣的结果。但是，仅使用比例来改善结果意味着资源消耗也会扩展。这种关系激发了对有效方法的研究，这些方法需要更少的资源才能获得相似的结果。这项调查涉及NLP效率的方法和发现，旨在指导该领域的新研究人员并激发新方法的发展。

我们研究了不同修剪技术对具有对比损失功能的深神经网络所学的表示的影响。我们的工作发现，相对于经过传统的跨透明损失训练的模型，在高稀疏度水平上，对比度学习的示例数量更高。为了理解这种明显的差异，我们使用派（Hooker等，2019），Q-Score（Kalibhat等，2022）和PD-Score（Baldock等，2021）等指标（Hooker等，2019），测量修剪对学习的表示质量的影响。我们的分析表明，修剪方法实施的时间表很重要。我们发现，当在训练阶段早期引入修剪时，稀疏性对学习表示的质量的负面影响最高。

事实证明，知识蒸馏是使用教师模型的预测来改善学生模型的一项有效技术。但是，最近的工作表明，在数据中的亚组中，平均效率的提高并不统一，尤其是在稀有亚组和类别上的准确性通常可能以准确性为代价。为了在可能遵循长尾分配的课程中保持强劲的表现，我们开发了蒸馏技术，这些技术是为了改善学生最差的级别表现而定制的。具体来说，我们为教师和学生介绍了不同组合的强大优化目标，并进一步允许在整体准确性和强大的最差目标之间进行任何权衡训练。我们从经验上表明，与其他基线方法相比，我们强大的蒸馏技术不仅可以实现更好的最差级别性能，而且还可以改善整体性能和最差的级别性能之间的权衡。从理论上讲，我们提供有关在目标培训健壮学生时使一名好老师的见解。

在机器学习中，一个极大的兴趣问题是了解哪些示例对于模型进行分类是有挑战性的。确定非典型示例可确保模型的安全部署，隔离需要进一步检查的样本，并为模型行为提供解释性。在这项工作中，我们提出梯度（VOG）的差异为有价值和有效的度量，以通过难度对数据进行排名，并浮出水面最具挑战性的人类审计示例的可行子集。我们表明，对于模型而言，具有较高VOG分数的数据点要在损坏或记忆的示例上学习和过度索引。此外，将评估限制为具有最低VOG的测试集实例，可以改善模型的泛化性能。最后，我们证明VOG是分布外检测的有价值和有效的排名。

We propose an empirical measure of the approximate accuracy of feature importance estimates in deep neural networks. Our results across several large-scale image classification datasets show that many popular interpretability methods produce estimates of feature importance that are not better than a random designation of feature importance. Only certain ensemble based approaches-VarGrad and SmoothGrad-Squared-outperform such a random assignment of importance. The manner of ensembling remains critical, we show that some approaches do no better then the underlying method but carry a far higher computational burden.

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications.

National Association of Securities Dealers Automated Quotations(NASDAQ) is an American stock exchange based. It is one of the most valuable stock economic indices in the world and is located in New York City \cite{pagano2008quality}. The volatility of the stock market and the influence of economic indicators such as crude oil, gold, and the dollar in the stock market, and NASDAQ shares are also affected and have a volatile and chaotic nature \cite{firouzjaee2022lstm}.In this article, we have examined the effect of oil, dollar, gold, and the volatility of the stock market in the economic market, and then we have also examined the effect of these indicators on NASDAQ stocks. Then we started to analyze the impact of the feedback on the past prices of NASDAQ stocks and its impact on the current price. Using PCA and Linear Regression algorithm, we have designed an optimal dynamic learning experience for modeling these stocks. The results obtained from the quantitative analysis are consistent with the results of the qualitative analysis of economic studies, and the modeling done with the optimal dynamic experience of machine learning justifies the current price of NASDAQ shares.

Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal information or unsightly objects. Such removal is not easily achieved with the current NeRF editing frameworks. We propose a framework to remove objects from a NeRF representation created from an RGB-D sequence. Our NeRF inpainting method leverages recent work in 2D image inpainting and is guided by a user-provided mask. Our algorithm is underpinned by a confidence based view selection procedure. It chooses which of the individual 2D inpainted images to use in the creation of the NeRF, so that the resulting inpainted NeRF is 3D consistent. We show that our method for NeRF editing is effective for synthesizing plausible inpaintings in a multi-view coherent manner. We validate our approach using a new and still-challenging dataset for the task of NeRF inpainting.