Very large language models such as GPT-3 have shown impressive performance across a wide variety of tasks, including text summarization. In this paper, we show that this strong performance extends to opinion summarization. We explore several pipeline methods for applying GPT-3 to summarize a large collection of user reviews in a zero-shot fashion, notably approaches based on recursive summarization and selecting salient content to summarize through supervised clustering or extraction. On two datasets, an aspect-oriented summarization dataset of hotel reviews and a generic summarization dataset of Amazon and Yelp reviews, we show that the GPT-3 models achieve very strong performance in human evaluation. We argue that standard evaluation metrics do not reflect this, and evaluate against several new measures targeting faithfulness, factuality, and genericity to contrast these different methods.

GPT-3等模型的零和少量提示的最新成功导致了NLP研究的范式转移。在本文中，我们研究了其对文本摘要的影响，重点是新闻摘要的经典基准领域。首先，我们研究了零击GPT-3与在大型摘要数据集中训练的微调模型的比较。我们表明，不仅人类压倒性地更喜欢GPT-3摘要，而且这些摘要也不遭受普通数据集特异性问题（例如事实差的问题）。接下来，我们研究这对评估意味着什么，尤其是黄金标准测试集的作用。我们的实验表明，基于参考和无参考的自动指标，例如最近提出的基于质量检查或基于质量的事实方法无法可靠地评估零击摘要。最后，我们讨论了未来的研究挑战，除了通用摘要之外，特别是基于关键字和方面的摘要，表明了优势微调方法与零拍的提示相比如何。为了支持进一步的研究，我们发布：（a）在4个标准摘要基准中，从微调和零摄像模型中产生的10K生成的摘要，（b）1K人类偏好判断和比较不同系统的普通系统，以进行通用和关键字的不同系统。基于摘要。

神经文本生成模型，如用于总结和翻译的那些模型产生高质量的输出，但是当我们真正想要的是一个不同的选项时，通常会集中在模式周围。我们介绍了一个搜索算法来构建编码大量生成选项的格子。首先，我们将解码重组为最佳搜索，该搜索探讨了与光束搜索不同的空间，并通过避免修剪路径来提高效率。其次，我们重新审视假设重组的想法：我们可以在搜索期间识别类似的生成候选者，并将它们合并为近似。在摘要和机器翻译中，我们表明我们的算法编码了数百到数千个不同的选项，这些选项保持语法和高质量成一个线性型格子。该算法为在大规模不同输出之上构建下游生成应用提供了基础。

虽然通过简单的因素问题回答，文本理解的大量进展，但更加全面理解话语仍然存在重大挑战。批判性地反映出文本的人将造成好奇心驱动，通常是开放的问题，这反映了对内容的深刻理解，并要求复杂的推理来回答。建立和评估这种类型的话语理解模型的关键挑战是缺乏注释数据，特别是因为找到了这些问题的答案（可能根本不回答），需要高度的注释载荷的高认知负荷。本文提出了一种新的范式，使可扩展的数据收集能够针对新闻文件的理解，通过话语镜头查看这些问题。由此产生的语料库DCQA（疑问回答的话语理解）包括在607名英语文件中的22,430个问题答案对组成。 DCQA以自由形式，开放式问题的形式捕获句子之间的话语和语义链接。在评估集中，我们向问题上的问题提交了来自好奇数据集的问题，我们表明DCQA提供了有价值的监督，以回答开放式问题。我们还在使用现有的问答资源设计预训练方法，并使用合成数据来适应不可批售的问题。

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.

There is no settled universal 3D representation for geometry with many alternatives such as point clouds, meshes, implicit functions, and voxels to name a few. In this work, we present a new, compelling alternative for representing shapes using a sequence of cross-sectional closed loops. The loops across all planes form an organizational hierarchy which we leverage for autoregressive shape synthesis and editing. Loops are a non-local description of the underlying shape, as simple loop manipulations (such as shifts) result in significant structural changes to the geometry. This is in contrast to manipulating local primitives such as points in a point cloud or a triangle in a triangle mesh. We further demonstrate that loops are intuitive and natural primitive for analyzing and editing shapes, both computationally and for users.

We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zero-shot transfer setting without the need for any fine-tuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.

A diffusion model learns to predict a vector field of gradients. We propose to apply chain rule on the learned gradients, and back-propagate the score of a diffusion model through the Jacobian of a differentiable renderer, which we instantiate to be a voxel radiance field. This setup aggregates 2D scores at multiple camera viewpoints into a 3D score, and repurposes a pretrained 2D model for 3D data generation. We identify a technical challenge of distribution mismatch that arises in this application, and propose a novel estimation mechanism to resolve it. We run our algorithm on several off-the-shelf diffusion image generative models, including the recently released Stable Diffusion trained on the large-scale LAION dataset.

在对关键安全环境的强化学习中，通常希望代理在所有时间点（包括培训期间）服从安全性限制。我们提出了一种称为Spice的新型神经符号方法，以解决这个安全的探索问题。与现有工具相比，Spice使用基于符号最弱的先决条件的在线屏蔽层获得更精确的安全性分析，而不会不适当地影响培训过程。我们在连续控制基准的套件上评估了该方法，并表明它可以达到与现有的安全学习技术相当的性能，同时遭受较少的安全性违规行为。此外，我们提出的理论结果表明，在合理假设下，香料会收敛到最佳安全政策。

激光雷达传感器是在未知环境中同时定位和映射（SLAM）的强大工具，但是它们产生的原始点云是密集的，计算量昂贵，并且不适合下游自治任务（例如运动计划）直接使用。为了与运动计划集成，希望大满贯管道生成轻量级的几何图表示。这样的表示也特别适合人造环境，通常可以将其视为在笛卡尔网格上建造的所谓“曼哈顿世界”。在这项工作中，我们为曼哈顿世界环境提出了一种3D激光雷达大满贯算法，该算法从点云中提取平面特征，以实现轻便，实时的定位和映射。我们的方法生成基于平面的地图，其记忆占其位置的记忆力明显少得多，并且适合于快速碰撞检查运动计划。通过利用曼哈顿世界的假设，我们靶向正交平面的提取，以生成比现有基于平面的LIDAR SLAM方法更结构化和组织的地图。我们证明了我们在高保真的AirSim模拟器以及配备有速蛋白底激光片的地面漫游车的现实实验中。在这两种情况下，我们都能够以匹配10 Hz的传感器速率的速率生成高质量的图和轨迹估计值。