标题： NeoBabel：一种多语言开放塔用于视觉生成 显示英文标题

标题： NeoBabel: A Multilingual Open Tower for Visual Generation

摘要： 文本到图像生成的进步主要以英语为中心，这给非英语使用者带来了障碍，并加剧了数字不平等。虽然现有系统依赖于翻译流程，但这些流程会引入语义偏差、计算开销和文化不一致。我们引入了NeoBabel，这是一种新的多语言图像生成框架，在性能、效率和包容性方面设定了新的帕累托前沿，支持六种语言：英语、中文、荷兰语、法语、印地语和波斯语。该模型通过大规模多语言预训练和高分辨率指令微调进行训练。为了评估其能力，我们将两个仅限英语的基准扩展为多语言版本：m-GenEval和m-DPG。NeoBabel在多语言性能上达到了最先进水平，同时保留了强大的英语能力，在m-GenEval上得分为0.75，在m-DPG上得分为0.68。值得注意的是，它在英语任务上的表现与领先模型相当，而在多语言基准测试中分别超过了它们+0.11和+0.09，尽管这些模型是基于多语言基础大语言模型构建的。这证明了我们针对对齐训练的有效性，可以保持并扩展跨语言泛化能力。我们进一步引入了两个新的指标，以严格评估多语言对齐和对混合代码提示的鲁棒性。值得注意的是，NeoBabel的表现与仅限英语的模型相当，甚至更优，而模型规模仅为它们的2-4倍。我们发布了一个开源工具包，包括所有代码、模型检查点、一个包含1.24亿个多语言文本-图像对的精选数据集以及标准化的多语言评估协议，以推动包容性人工智能研究。我们的工作表明，多语言能力不是一种权衡，而是提升生成式人工智能的鲁棒性、效率和文化真实性的催化剂。 显示英文摘要

摘要： Text-to-image generation advancements have been predominantly English-centric, creating barriers for non-English speakers and perpetuating digital inequities. While existing systems rely on translation pipelines, these introduce semantic drift, computational overhead, and cultural misalignment. We introduce NeoBabel, a novel multilingual image generation framework that sets a new Pareto frontier in performance, efficiency and inclusivity, supporting six languages: English, Chinese, Dutch, French, Hindi, and Persian. The model is trained using a combination of large-scale multilingual pretraining and high-resolution instruction tuning. To evaluate its capabilities, we expand two English-only benchmarks to multilingual equivalents: m-GenEval and m-DPG. NeoBabel achieves state-of-the-art multilingual performance while retaining strong English capability, scoring 0.75 on m-GenEval and 0.68 on m-DPG. Notably, it performs on par with leading models on English tasks while outperforming them by +0.11 and +0.09 on multilingual benchmarks, even though these models are built on multilingual base LLMs. This demonstrates the effectiveness of our targeted alignment training for preserving and extending crosslingual generalization. We further introduce two new metrics to rigorously assess multilingual alignment and robustness to code-mixed prompts. Notably, NeoBabel matches or exceeds English-only models while being 2-4x smaller. We release an open toolkit, including all code, model checkpoints, a curated dataset of 124M multilingual text-image pairs, and standardized multilingual evaluation protocols, to advance inclusive AI research. Our work demonstrates that multilingual capability is not a trade-off but a catalyst for improved robustness, efficiency, and cultural fidelity in generative AI.