用三个专用适配器提升多语言摘要与问答性能
PSK at WMT 2026 MIST: Task-Specialized QLoRA Adapters for Multilingual Summarization and Question Answering
- 为摘要、问答等任务分别设计轻量适配器
- 自建数据训练的适配器优于组织方提供的多任务模型
- 适合需要多任务精调的低资源多语言应用
本文介绍我们对WMT 2026多语言指令共享任务的PSK提交方案。系统基于3.35亿参数的Tiny Aya Global模型，配备三个QLoRA适配器，分别针对摘要、段落问答和过滤后的独立问答任务。摘要数据包含作者撰写摘要的科学论文。在预留测试集上，上下文与摘要适配器表现优于仅使用组织方数据训练的多任务适配器。开放型问答结果表现不一，受答案长度和评估方式影响，因此我们提交了三套系统：共用上下文与摘要适配器，但采用不同开放问答适配器。
原文摘要 · Abstract (English)
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.
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