Abstract
This paper presents team Kl33n3x's multilingual dialogue summarization and question answering system developed for the NLPAI4Health 2025 shared task. The approach employs a three-stage pipeline: forward translation from Indic languages to English, multitask text generation using a 2.55B parameter distilled language model, and reverse translation back to source languages. By leveraging knowledge distillation techniques, this work demonstrates that compact models can achieve highly competitive performance across nine languages. The system achieved strong win rates across the competition's tasks, with particularly robust performance on Marathi (86.7% QnA), Tamil (86.7% QnA), and Hindi (80.0% QnA), demonstrating the effectiveness of translation-based approaches for low-resource language processing without task-specific fine-tuning.
Abstract (translated)
本文介绍了团队Kl33n3x为NLPAI4Health 2025共享任务开发的多语言对话摘要和问答系统。该方法采用三阶段管道：将印地语系语言翻译成英语，使用一个2.55B参数精简的语言模型进行多任务文本生成，以及返回源语言的逆向翻译。通过利用知识蒸馏技术，这项工作展示了紧凑型模型在九种语言中可以实现非常有竞争力的表现。该系统在整个竞赛任务中取得了强大的胜率，在马拉地语（问答部分86.7%）、泰卢固语（问答部分86.7%）和印地语（问答部分80.0%）上的表现尤为出色，证明了基于翻译的方法在处理资源不足的语言时的有效性，并且无需针对特定任务进行微调。
URL
https://arxiv.org/abs/2601.09059
