Knowledge representation and reasoning in law are essential to facilitate the automation of legal analysis and decision-making tasks. In this paper, we propose a new approach based on legal science, specifically legal taxonomy, for representing and reasoning with legal documents. Our approach interprets the regulations in legal documents as binary trees, which facilitates legal reasoning systems to make decisions and resolve logical contradictions. The advantages of this approach are twofold. First, legal reasoning can be performed on the basis of the binary tree representation of the regulations. Second, the binary tree representation of the regulations is more understandable than the existing sentence-based representations. We provide an example of how our approach can be used to interpret the regulations in a legal document.
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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.
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成功的人工智能系统通常需要大量标记的数据来从文档图像中提取信息。在本文中，我们研究了改善人工智能系统在理解文档图像中的性能的问题，尤其是在培训数据受到限制的情况下。我们通过使用加强学习提出一种新颖的填充方法来解决问题。我们的方法将信息提取模型视为策略网络，并使用策略梯度培训来更新模型，以最大程度地提高补充传统跨凝结损失的综合奖励功能。我们使用标签和专家反馈在四个数据集上进行的实验表明，我们的填充机制始终提高最先进的信息提取器的性能，尤其是在小型培训数据制度中。
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编写代码时，大多数程序员会犯错误。这些错误中的一些很小，几乎不需要对原始程序进行编辑 - 最近称为最后一个英里错误的错误。这些错误打破了经验丰富的开发人员的流程，并且可以使新手程序员陷入困境。针对此类错误的现有自动化维修技术是特定于域的，并且不容易延续到新域。转移符号方法需要实质性的工程和神经方法需要数据和重新培训。我们介绍RING，这是一种多语言维修引擎，该引擎由经过代码训练的大型语言模型（例如Codex）提供动力。这样的多语言引擎可以为编程援助提供一个翻转的模型，该模型与传统的代码建议技术相比，程序员编写代码和AI援助建议修复。从程序员手动修复错误的方式中汲取灵感，我们表明，基于迅速的策略将修复作为本地化，转换和候选排名概念化，可以成功地在多个域中成功维修程序，但努力最少。我们通过评估6个不同的域并将性能与域特异性维修引擎进行比较，为这种多语言维修引擎提供了第一个结果。我们表明，环可以超过这些域中3个域中的特定于域特异性修复引擎。我们还确定了使用LLMC进行多语言维修的未来研究方向。
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组织病理学图像的出现取决于组织类型，染色和数字化过程。这些因素因来源而异，是域转移问题的潜在原因。由于这个问题，尽管深度学习模型在计算病理学中取得了巨大的成功，但在特定领域训练的模型当我们将其应用于另一个领域时，仍可能会表现出色。为了克服这一点，我们提出了一种称为PatchShuffling的新扩展，并为预训练的深度学习模型而被称为Impash的新型自我监视的对比学习框架。使用这些，我们获得了一个RESNET50编码器，该编码器可以提取对域移位抗性的图像表示。我们通过使用其他域普通化技术来比较了我们的派生表示形式，它们通过将它们用于结直肠组织图像的跨域分类。我们表明，所提出的方法优于其他传统的组织学领域适应和最先进的自我监督学习方法。代码可在以下网址获得：https：//github.com/trinhvg/impash。
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电子表格广泛用于桌面操作和演示。这些表的风格格式是演示和分析的重要属性。结果，流行的电子表格软件（例如Excel）支持基于数据依赖性规则的自动格式表。不幸的是，编写这些格式规则对于用户来说可能是具有挑战性的，因为这需要了解基础规则语言和数据逻辑。在本文中，我们提出了Cornet，这是一种神经符号系统，该系统解决了从格式化细胞的用户示例中自动学习此类格式规则的新问题。 Cornet从归纳计划的合成中汲取灵感，并根据半监督聚类和迭代决策树学习结合了符号规则，并与神经排名者一起产生条件格式的规则。为了激励和评估我们的方法，我们从超过40k真实电子表格的语料库中提取了表格的表格。使用这些数据，我们将短号与各种符号和神经基线进行了比较。我们的结果表明，与这些基线相比，Cornet可以在不同条件下更准确地学习规则。除了从用户示例中学习规则外，我们还提出了两个案例研究，以激发Cornet的其他用途：简化用户条件格式规则并恢复规则，即使用户可能手动格式化了其数据。
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大多数低编码平台的用户，例如Excel和PowerApps，都以特定于域的公式语言编写程序来执行非平凡的任务。用户通常可以编写他们想要的大部分程序，但是引入了一些小错误，这些错误会产生破损的公式。这些错误既可以是句法和语义，也很难让低代码用户识别和修复，即使只能通过一些编辑解决。我们正式化了产生最后一英里维修问题等编辑的问题。为了解决这个问题，我们开发了Lamirage，这是一种最后一英里的维修发动机发电机，结合了符号和神经技术，以低代码公式语言进行最后一英里维修。 Lamirage采用语法和一组特定领域的约束/规则，它们共同近似目标语言，并使用它们来生成可以用该语言修复公式的维修引擎。为了应对本地化错误和对候选维修进行排名的挑战，Lamirage利用神经技术，而它依赖于符号方法来生成候选维修。这种组合使Lamirage可以找到满足提供的语法和约束的维修，然后选择最自然的修复。我们将Lamirage与400个Real Excel和PowerFX公式的最新神经和符号方法进行了比较，其中Lamirage的表现优于所有基线。我们释放这些基准，以鼓励在低代码域中进行后续工作。
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Domain adaptation has been vastly investigated in computer vision but still requires access to target images at train time, which might be intractable in some conditions, especially for long-tail samples. In this paper, we propose the task of `Prompt-driven Zero-shot Domain Adaptation', where we adapt a model trained on a source domain using only a general textual description of the target domain, i.e., a prompt. First, we leverage a pretrained contrastive vision-language model (CLIP) to optimize affine transformations of source features, bringing them closer to target text embeddings, while preserving their content and semantics. Second, we show that augmented features can be used to perform zero-shot domain adaptation for semantic segmentation. Experiments demonstrate that our method significantly outperforms CLIP-based style transfer baselines on several datasets for the downstream task at hand. Our prompt-driven approach even outperforms one-shot unsupervised domain adaptation on some datasets, and gives comparable results on others. The code is available at https://github.com/astra-vision/PODA.
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This paper presents the development of an AI-based language learning platform Revita. It is a freely available intelligent online tutor, developed to support learners of multiple languages, from low-intermediate to advanced levels. It has been in pilot use by hundreds of students at several universities, whose feedback and needs are shaping the development. One of the main emerging features of Revita is the introduction of a system of linguistic constructs as the representation of domain knowledge. The system of constructs is developed in close collaboration with experts in language teaching. Constructs define the types of exercises, the content of the feedback, and enable the detailed modeling and evaluation of learning progress.
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Temporal Graph Neural Network (TGNN) has been receiving a lot of attention recently due to its capability in modeling time-evolving graph-related tasks. Similar to Graph Neural Networks, it is also non-trivial to interpret predictions made by a TGNN due to its black-box nature. A major approach tackling this problems in GNNs is by analyzing the model' responses on some perturbations of the model's inputs, called perturbation-based explanation methods. While these methods are convenient and flexible since they do not need internal access to the model, does this lack of internal access prevent them from revealing some important information of the predictions? Motivated by that question, this work studies the limit of some classes of perturbation-based explanation methods. Particularly, by constructing some specific instances of TGNNs, we show (i) node-perturbation cannot reliably identify the paths carrying out the prediction, (ii) edge-perturbation is not reliable in determining all nodes contributing to the prediction and (iii) perturbing both nodes and edges does not reliably help us identify the graph's components carrying out the temporal aggregation in TGNNs.
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