The vision of an inclusive World Wide Web is impeded by a severe linguistic divide, particularly for communities in low-resource regions of Southeast Asia. While large language models (LLMs) offer a potential solution for translation, their deployment in data-poor contexts faces a dual challenge: the scarcity of high-quality, culturally relevant data and the prohibitive energy costs of training on massive, noisy web corpora. To resolve the tension between digital inclusion and environmental sustainability, we introduce Sustainable Agent-Guided Expert-tuning (SAGE). This framework pioneers an energy-aware paradigm that prioritizes the "right data" over "big data". Instead of carbon-intensive training on unfiltered datasets, SAGE employs a reinforcement learning (RL) agent, optimized via Group Relative Policy Optimization (GRPO), to autonomously curate a compact training set. The agent utilizes a semantic reward signal derived from a small, expert-constructed set of community dialogues to filter out noise and cultural misalignment. We then efficiently fine-tune open-source LLMs on this curated data using Low-Rank Adaptation (LoRA). We applied SAGE to translation tasks between English and seven low-resource languages (LRLs) in Southeast Asia. Our approach establishes new state-of-the-art performance on BLEU-4 and COMET-22 metrics, effectively capturing local linguistic nuances. Crucially, SAGE surpasses baselines trained on full datasets while reducing data usage by 97.1% and training energy consumption by 95.2%. By delivering high-performance models with a minimal environmental footprint, SAGE offers a scalable and responsible pathway to bridge the digital divide in the Global South.

翻译：摘要：构建包容性万维网的愿景受到严重的语言鸿沟阻碍，尤其是针对东南亚低资源地区的社群。尽管大语言模型为翻译提供了潜在解决方案，但在数据匮乏场景下的部署面临双重挑战：高质量、文化适配数据的稀缺，以及在海量混杂网络语料上进行训练导致的过高能源成本。为调和数字包容性与环境可持续性之间的矛盾，我们提出可持续智能体引导专家调优方法（SAGE）。该框架开创了一种能效感知范式，优先选择"正确数据"而非"大数据"。SAGE摒弃在高碳足迹的非筛选数据集上训练的传统方式，采用经群组相对策略优化（GRPO）优化的强化学习智能体，自主筛选紧凑训练集。该智能体利用从少量专家构建的社区对话中提取的语义奖励信号，过滤噪音和文化错位样本。随后通过低秩自适应（LoRA）技术，在筛选数据上高效微调开源大语言模型。我们将SAGE应用于英语与东南亚七种低资源语言之间的翻译任务。该方法在BLEU-4和COMET-22指标上创下最新最优性能，有效捕捉了当地语言细微差异。尤为关键的是，SAGE在数据使用量减少97.1%、训练能耗降低95.2%的情况下，仍超越基于完整数据集训练的基线模型。通过以最小环境足迹实现高性能模型，SAGE为缩小全球南方数字鸿沟提供了可扩展、负责任的解决方案。