The BLOOM model is a large open-source multilingual language model capable of zero-shot learning, but its pretraining was limited to 46 languages. To improve its zero-shot performance on unseen languages, it is desirable to adapt BLOOM, but previous works have only explored adapting small language models. In this work, we apply existing language adaptation strategies to BLOOM and benchmark its zero-shot prompting performance on eight new languages. We find language adaptation to be effective at improving zero-shot performance in new languages. Surprisingly, adapter-based finetuning is more effective than continued pretraining for large models. In addition, we discover that prompting performance is not significantly affected by language specifics, such as the writing system. It is primarily determined by the size of the language adaptation data. We also add new languages to BLOOMZ, which is a multitask finetuned version of BLOOM capable of following task instructions zero-shot. We find including a new language in the multitask fine-tuning mixture to be the most effective method to teach BLOOMZ a new language. We conclude that with sufficient training data language adaptation can generalize well to diverse languages. Our code is available at \url{https://github.com/bigscience-workshop/multilingual-modeling/}.

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.

Bibletts是一种在撒哈拉以南非洲使用的十种语言的大型，高质量的开放语音数据集。该语料库包含每语言最多86个小时的对齐，工作室质量的48kHz单扬声器唱片，从而能够开发高质量的文本到语音模型。代表的十种语言是：Akuapem Twi，Asante Twi，Chichewa，Ewe，Hausa，Kikuyu，Lingala，Luganda，Luganda，Luo和Yoruba。该语料库是由Biblica的Open.Bible Project制作和发行的圣经录音的衍生作品。我们已经对齐，清洁和过滤了原始录音，并还对每种语言的对齐子进行了手工检查。我们为具有Coqui TTS的文本到语音模型提供了结果。数据是根据商业友好的CC-SA许可发布的。

对于自然语言处理中的许多任务，将知识从一个域转移到另一个领域至关重要，尤其是当目标域中的可用数据量受到限制时。在这项工作中，我们在指定实体识别（NER）的背景下提出了一种新颖的域适应方法。我们提出了一种两步方法，该方法由可变基本模块和模板模块组成，该模块在简单的描述模式的帮助下利用了预训练的语言模型中捕获的知识。我们的方法简单而通用，可以在几次射击和零拍设置中应用。评估我们在许多不同数据集中的轻量级方法表明，它可以将最新基准的性能提高2-5％的F1分数。

语言模型预训练的最新进展利用大规模数据集创建多语言模型。但是，这些数据集中大多遗漏了低资源语言。这主要是因为网络上没有很好地表示口语，因此被排除在用于创建数据集的大规模爬网中。此外，这些模型的下游用户仅限于最初选择用于预训练的语言的选择。这项工作调查了如何最佳利用现有的预培训模型来为16种非洲语言创建低资源翻译系统。我们关注两个问题：1）如何将预训练的模型用于初始预培训中未包含的语言？ 2）生成的翻译模型如何有效地转移到新域？为了回答这些问题，我们创建了一个新的非洲新闻语料库，涵盖16种语言，其中8种语言不属于任何现有评估数据集的一部分。我们证明，将两种语言转移到其他语言和其他领域的最有效策略是，以少量的高质量翻译数据微调大型预训练模型。

多语言预训练的语言模型（PLM）在高资源和低资源语言的下游任务上表现出令人印象深刻的表现。但是，在预培训期间，尤其是非洲语言中，看不见的语言仍然有很大的表现。适应新语言的最有效方法之一是\ textit {语言自适应微调}（LAFT） - 使用预训练目标对单语言的多语言PLM进行微调。但是，适应目标语言会单独使用大磁盘空间，并限制了由此产生的模型的跨语言转移能力，因为它们已经专门用于单语言。在本文中，我们对17种最重要的非洲语言和其他三种在非洲大陆上广泛使用的高资源语言对17种最具资源的非洲语言进行\ Textit {多语言自适应微调}，以鼓励跨语性转移学习。为了进一步专注于多语言PLM，我们从嵌入式层中删除了与MAFT之前的非非洲写作脚本相对应的词汇令牌，从而将模型大小降低了约50％。我们对两个多语言PLM（Afriberta和XLM-R）和三个NLP任务（NER，新闻主题分类和情感分类）的评估表明，我们的方法可以在单个语言上应用LAFT，同时需要较小的磁盘空间。此外，我们表明我们的适应性PLM还提高了参数有效微调方法的零击跨语性转移能力。

情感分析是NLP中研究最广泛的应用程序之一，但大多数工作都集中在具有大量数据的语言上。我们介绍了尼日利亚的四种口语最广泛的语言（Hausa，Igbo，Nigerian-Pidgin和Yor \'ub \'a）的第一个大规模的人类通知的Twitter情感数据集，该数据集由大约30,000个注释的推文组成（以及每种语言的大约30,000个）（以及14,000尼日利亚猎人），其中包括大量的代码混合推文。我们提出了文本收集，过滤，处理和标记方法，使我们能够为这些低资源语言创建数据集。我们评估了数据集上的预训练模型和转移策略。我们发现特定于语言的模型和语言适应性芬通常表现最好。我们将数据集，训练的模型，情感词典和代码释放到激励措施中，以代表性不足的语言进行情感分析。

差异私有随机梯度下降（DPSGD）是基于差分隐私（DP）范例的随机梯度下降的变化，这可以减轻来自在训练数据中存在敏感信息的隐私威胁。然而，具有DPSGD的培训深度神经网络的一个主要缺点是模型精度的降低。本文研究了标准化层对DPSGD性能的影响。我们证明标准化层显着影响了深度神经网络与嘈杂参数的效用，应该被视为DPSGD培训的基本成分。特别是，我们提出了一种新的方法，用于将批量标准化与DPSGD集成，而不会产生额外的隐私损失。通过我们的方法，我们能够培训更深的网络并实现更好的效用隐私权衡。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.