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.

We present NeRFEditor, an efficient learning framework for 3D scene editing, which takes a video captured over 360{\deg} as input and outputs a high-quality, identity-preserving stylized 3D scene. Our method supports diverse types of editing such as guided by reference images, text prompts, and user interactions. We achieve this by encouraging a pre-trained StyleGAN model and a NeRF model to learn from each other mutually. Specifically, we use a NeRF model to generate numerous image-angle pairs to train an adjustor, which can adjust the StyleGAN latent code to generate high-fidelity stylized images for any given angle. To extrapolate editing to GAN out-of-domain views, we devise another module that is trained in a self-supervised learning manner. This module maps novel-view images to the hidden space of StyleGAN that allows StyleGAN to generate stylized images on novel views. These two modules together produce guided images in 360{\deg}views to finetune a NeRF to make stylization effects, where a stable fine-tuning strategy is proposed to achieve this. Experiments show that NeRFEditor outperforms prior work on benchmark and real-world scenes with better editability, fidelity, and identity preservation.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

Frozen pretrained models have become a viable alternative to the pretraining-then-finetuning paradigm for transfer learning. However, with frozen models there are relatively few parameters available for adapting to downstream tasks, which is problematic in computer vision where tasks vary significantly in input/output format and the type of information that is of value. In this paper, we present a study of frozen pretrained models when applied to diverse and representative computer vision tasks, including object detection, semantic segmentation and video action recognition. From this empirical analysis, our work answers the questions of what pretraining task fits best with this frozen setting, how to make the frozen setting more flexible to various downstream tasks, and the effect of larger model sizes. We additionally examine the upper bound of performance using a giant frozen pretrained model with 3 billion parameters (SwinV2-G) and find that it reaches competitive performance on a varied set of major benchmarks with only one shared frozen base network: 60.0 box mAP and 52.2 mask mAP on COCO object detection test-dev, 57.6 val mIoU on ADE20K semantic segmentation, and 81.7 top-1 accuracy on Kinetics-400 action recognition. With this work, we hope to bring greater attention to this promising path of freezing pretrained image models.

二重优化（BO）可用于解决各种重要的机器学习问题，包括但不限于超参数优化，元学习，持续学习和增强学习。常规的BO方法需要通过与隐式分化的低级优化过程进行区分，这需要与Hessian矩阵相关的昂贵计算。最近，人们一直在寻求BO的一阶方法，但是迄今为止提出的方法对于大规模的深度学习应用程序往往是复杂且不切实际的。在这项工作中，我们提出了一种简单的一阶BO算法，仅取决于一阶梯度信息，不需要隐含的区别，并且对于大规模的非凸函数而言是实用和有效的。我们为提出的方法提供了非注重方法分析非凸目标的固定点，并提出了表明其出色实践绩效的经验结果。

视频预测是一个重要但充满挑战的问题。负担着生成未来框架和学习环境动态的任务。最近，通过将视频预测分为两个子问题：预训练图像生成器模型，随后学习图像生成器的潜在空间中的自动回归预测模型，可以将视频预测分为两个子问题，从而成为强大的视频预测工具。 。但是，成功产生高保真性和高分辨率视频尚待观察。在这项工作中，我们研究了如何培训自回归潜在的潜在视频预测模型，能够预测高保真的未来帧，并对现有模型进行最小的修改，并产生高分辨率（256x256）视频。具体而言，我们通过使用因果变压器模型采用高保真图像发生器（VQ-GAN）来扩展先前的模型，并引入TOP-K采样和数据增强的其他技术，以进一步提高视频预测质量。尽管简单起见，但提出的方法仍可以在标准视频预测基准的最新方法中实现竞争性能，而参数较少，并在复杂和大规模数据集上实现了高分辨率的视频预测。视频可从https://sites.google.com/view/harp-videos/home获得。

基于视觉模型的增强学习（RL）有可能从视觉观察中实现样品有效的机器人学习。然而，当前的方法通常会训练单个模型端到端，以学习视觉表示和动态，因此难以准确地对机器人和小物体之间的相互作用进行建模。在这项工作中，我们介绍了一个基于视觉模型的RL框架，该框架将视觉表示学习和动态学习取消。具体而言，我们训练具有卷积层和视觉变压器（VIT）的自动编码器，以重建具有掩盖卷积特征的像素，并学习一个潜在的动力学模型，该模型在自动编码器的表示形式上运行。此外，为了编码与任务相关的信息，我们为自动编码器引入了辅助奖励预测目标。我们使用环境互动收集的在线样本不断更新自动编码器和动态模型。我们证明，我们的去耦方法在Meta-World和rlbench的各种视觉机器人任务上实现了最先进的表现，例如，我们在Meta-World的50个视觉机器人操作任务上实现了81.7％的成功率，而元世界则达到了81.7％基线达到67.9％。代码可在项目网站上找到：https：//sites.google.com/view/mwm-rl。

我们介绍了一个大规模实验，该实验对编码器进行了预处理，其参数计数范围从700m到9.3b不等，随后蒸馏到较小的型号中，范围为17m-170亿参数，其应用到自然语言理解（NLU）组件（NLU）组件（虚拟助手系统。尽管我们使用70％的口语数据训练，但在对书面形式的跨语性自然语言推论（XNLI）语料库进行评估时，我们的教师模型与XLM-R和MT5相当。我们使用系统中的内域数据对教师模型进行了第二阶段的训练，以提高了3.86％的相对分类，而相对7.01％的插槽填充。我们发现，即使是从我们的2阶段教师模型中提取的170亿参数模型，与仅接受公共数据的2.3B参数老师相比，与2.3B参数老师相比，意图分类更好2.88％，并且7.69％的插槽填充错误率更好（第1阶段），强调了。内域数据对训练的重要性。当使用标记的NLU数据进行离线评估时，我们的17m参数阶段2蒸馏模型的表现分别优于XLM-R碱基（85m Params）和Distillbert（42m Params），分别优于4.23％至6.14％。最后，我们介绍了一个完整的虚拟助手实验平台的结果，在该平台中，我们发现使用经过预训练和蒸馏管道训练的模型超过了从8500万参数教师蒸馏的模型，在自动测量全系统用户不满的自动测量中，从8500万参数教师蒸馏出3.74％-4.91％。

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。