In-context learning (ICL) enables large language models (LLMs) to perform new tasks by prompting them with a sequence of training examples. However, ICL is very sensitive to the choice of training examples: randomly sampling examples from a training set leads to high variance in performance. In this paper, we show that curating a carefully chosen subset of training data greatly stabilizes ICL performance. We propose two methods to choose training subsets, both of which score training examples individually and then select the highest-scoring ones. CondAcc scores a training example by its average ICL accuracy when combined with random training examples, while Datamodels learns a linear proxy model that estimates how the presence of each training example influences LLM accuracy. On average, CondAcc and Datamodels outperform sampling from the entire training set by 7.7% and 6.3%, respectively, across 5 tasks and two LLMs. Our analysis shows that stable subset examples are no more diverse than average, and are not outliers in terms of sequence length and perplexity.

In many task settings, text classification models are likely to encounter examples from novel classes on which they cannot predict correctly. Selective prediction, in which models abstain on low-confidence examples, provides a possible solution, but existing models are often overly confident on OOD examples. To remedy this overconfidence, we introduce Contrastive Novelty-Augmented Learning (CoNAL), a two-step method that generates OOD examples representative of novel classes, then trains to decrease confidence on them. First, we generate OOD examples by prompting a large language model twice: we prompt it to enumerate relevant novel labels, then generate examples from each novel class matching the task format. Second, we train our classifier with a novel contrastive objective that encourages lower confidence on generated OOD examples than training examples. When trained with CoNAL, classifiers improve in their ability to detect and abstain on OOD examples over prior methods by an average of 2.3% AUAC and 5.5% AUROC across 4 NLP datasets, with no cost to in-distribution accuracy.

最近，已证明模型的神经网络模型可以提高计算机视觉和增强学习任务的样本效率。本文在机器人策略学习的背景下探讨了这一想法，在这种情况下，必须完全在物理机器人系统上学习策略，而无需参考模型，模拟器或离线数据集。我们专注于模棱两可的SAC在机器人操作中的应用，并探索算法的许多变化。最终，我们证明了通过在不到一小时或两个小时的壁时钟时间内的机上体验完全学习几项非平凡操纵任务的能力。

问题回答（QA）对知识库（KBS）的挑战是充满挑战的，因为所需的推理模式多样化，本质上是无限的，类型的推理模式。但是，我们假设以大型KB为基础，以回答各自子图中各个实体的查询类型所需的推理模式。利用不同子图的本地社区之间的这种结构相似性，我们引入了一个半参数模型（cbr-subg），（i）一个非参数组件，每个查询，每个查询，都会动态检索其他类似的$ k $ - $ - $ - $ - near-neart-tebrienk（KNN）培训查询以及查询特定的子图和（ii）训练的参数组件，该参数分量可以从KNN查询的子图中识别（潜在的）推理模式，然后将其应用于目标查询的子图。我们还提出了一种自适应子图收集策略，以选择特定于查询的compact子图，从而使我们可以扩展到包含数十亿个事实的完整freebase kb。我们表明，CBR-SUBG可以回答需要子图推理模式的查询，并在几个KBQA基准上的最佳模型竞争性能。我们的子图收集策略还会产生更多紧凑的子图（例如，webQSP的尺寸减小55 \％，而将答案召回的召回率增加4.85 \％）\ footNote {代码，模型和子码头可在\ url {https://github.com上获得。 /rajarshd/cbr-subg}}。

在动态对抗数据收集（DADC）中，人类的注释者是任务的，找到模型努力预测的示例。已经显示出在达克收集的训练数据上培训的模型在对抗和域外设置方面更加强大，并且对于人类来说更难愚弄。然而，DADC比传统数据收集更耗时，因此每个示例更昂贵。在这项工作中，我们检查我们是否可以保持DADC的优势，而不会遭受额外的成本。为此，我们引入了生成的注释助理（GaAs），生成的循环模型，提供了注释器完全批准，修改或拒绝的实时建议。我们在20个实验设置中收集培训数据集，并对这种方法进行详细分析，用于标准和对冲数据收集的提取问题应答（QA）的任务。我们展示了GaAs在注释速度方面提供了显着的效率效益，同时导致改善模型愚蠢的速率。此外，我们还表明，GaA辅助数据在回答任务的各种问题上导致更高的下游模型性能。

为了创建在广泛的测试输入中强大的模型，培训数据集应包括跨越许多现象的各种示例。动态的对抗数据收集（DADC），注释者制作的示例挑战不断改进模型的示例，其有望是生成这种多样化训练集的一种方法。先前的工作表明，在1-3轮中运行DADC可以帮助模型修复某些错误类型，但不一定会带来更好的概括，而不是对抗性测试数据。我们认为，在许多回合中运行DADC可以最大化其训练时间的好处，因为不同的回合可以涵盖许多与任务相关的现象。我们介绍了长期DADC的第一个研究，其中我们收集了20轮NLI示例，用于一小部分前提，并采用对抗性和非对抗性方法。与接受非对抗数据的训练的模型相比，接受DADC示例培训的模型在我们的专家策划测试集中的错误少26％。我们的分析表明，DADC产生的例子更加困难，更词法和句法多样性，并且与非对抗性示例相比，注释伪像更少。

Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. Existing datasets either focus exclusively on answerable questions, or use automatically generated unanswerable questions that are easy to identify. To address these weaknesses, we present SQuAD 2.0, the latest version of the Stanford Question Answering Dataset (SQuAD). SQuAD 2.0 combines existing SQuAD data with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD 2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. SQuAD 2.0 is a challenging natural language understanding task for existing models: a strong neural system that gets 86% F1 on SQuAD 1.1 achieves only 66% F1 on SQuAD 2.0.

Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear.To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of 75% F1 score to 36%; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to 7%. We hope our insights will motivate the development of new models that understand language more precisely. Article: Super Bowl 50 Paragraph: "Peyton Manning became the first quarterback ever to lead two different teams to multiple Super Bowls. He is also the oldest quarterback ever to play in a Super Bowl at age 39. The past record was held by John Elway, who led the Broncos to victory in Super Bowl XXXIII at age 38 and is currently Denver's Executive Vice President of Football Operations and General Manager. Quarterback Jeff Dean had jersey number 37 in Champ Bowl XXXIV." Question: "What is the name of the quarterback who was 38 in Super Bowl XXXIII?"

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.