PSK在WMT 2026 MIST：面向多语言摘要与问答的任务专用QLoRA适配器
PSK at WMT 2026 MIST: Task-Specialized QLoRA Adapters for Multilingual Summarization and Question Answering
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中文总结 AI 辅助
该研究针对WMT 2026多语言指令共享任务，基于Tiny Aya Global模型构建三个QLoRA适配器，分别适配摘要、段落问答、独立问答任务，对比验证了不同适配器的性能并提交含不同开放问答适配器的系统。
中文摘要 AI 辅助
我们介绍了PSK向WMT 2026多语言指令共享任务提交的方案。我们的系统采用参数规模为33.5亿的Tiny Aya Global模型，搭配三个QLoRA适配器，每个适配器对应一项任务。这些适配器分别在多语言文档-摘要对、基于段落的问答、以及经过筛选的独立问答数据上进行训练。摘要任务的数据还包含科学论文及其作者撰写的摘要。在我们的留存验证拆分数据上，上下文适配器和摘要适配器的表现优于仅在组织者提供的数据上训练的多任务适配器。开放问答的结果参差不齐，且随答案长度和评估方法变化。因此我们提交三个系统，它们使用相同的上下文适配器和摘要适配器，但采用不同的开放问答适配器。
英文摘要
We describe the PSK submission to the WMT 2026 Multilingual Instruction Shared Task. Our system uses the 3.35B-parameter Tiny Aya Global model with three QLoRA adapters, one for each task. The adapters are trained on multilingual document-summary pairs, passage-based question answering, and filtered standalone question answering. The summarization data also includes scientific papers with their author-written abstracts. On our held-out split, the context and summarization adapters perform better than our multitask adapter, which was trained only on data supplied by the organizers. Results for open QA are mixed and vary with answer length and evaluation method. We therefore submit three systems with the same context and summarization adapters but different open-QA adapters.
