The use of emojis affords a visual modality to, often private, textual communication. The task of predicting emojis however provides a challenge for machine learning as emoji use tends to cluster into the frequently used and the rarely used emojis. Much of the machine learning research on emoji use has focused on high resource languages and has conceptualised the task of predicting emojis around traditional server-side machine learning approaches. However, traditional machine learning approaches for private communication can introduce privacy concerns, as these approaches require all data to be transmitted to a central storage. In this paper, we seek to address the dual concerns of emphasising high resource languages for emoji prediction and risking the privacy of people's data. We introduce a new dataset of $118$k tweets (augmented from $25$k unique tweets) for emoji prediction in Hindi, and propose a modification to the federated learning algorithm, CausalFedGSD, which aims to strike a balance between model performance and user privacy. We show that our approach obtains comparative scores with more complex centralised models while reducing the amount of data required to optimise the models and minimising risks to user privacy.

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.

仇恨言语检测模型通常在持有的测试集上评估。但是，这有可能因为仇恨言语数据集中越来越有据可查的系统差距和偏见，因此绘制模型性能的不完整且潜在的误导性图片。为了实现更多针对性的诊断见解，最近的研究引入了仇恨言语检测模型的功能测试。但是，这些测试目前仅针对英语内容，这意味着它们无法支持全球数十亿语言所说的其他语言中更有效模型的开发。为了帮助解决这个问题，我们介绍了多语言Hatecheck（MHC），这是一套用于多语言仇恨言语检测模型的功能测试。 MHC涵盖了跨十种语言的34个功能，这比任何其他仇恨语音数据集更多。为了说明MHC的效用，我们训练和测试了高性能的多语言仇恨语音检测模型，并揭示了单语和跨语性应用的关键模型弱点。

道德是人类最长的智力努力之一。近年来，AI和NLP的领域试图撰写与学习系统的与人类相互作用的学习系统，应该被限制为行为道德。该静脉中的一个提议是建立道德模型，可以采取任意文本，并输出关于所描述的情况的道德判断。在这项工作中，我们专注于对最近提出的Delphi模型的单一案例研究，并为该项目的建议自动化道德判决提供了批评。通过对Delphi的审计，我们检查更广泛的问题，适用于任何类似的尝试。我们讨论了机器道德如何通过专注于技术的当前和近期使用技术的方式来讨论机器伦理，以透明度，民主价值观，并允许直接的责任。