NLP4Health: Multilingual Clinical Dialogue Summarization and QA with mT5 and LoRA

AbstractIn this work, we present NLP4Health, a unified and reproducible pipeline to accomplish the tasks of multilingual clinical dialogue summarization and question answering (QA). Our system fine-tunes the multilingual sequence-to-sequence model google/mt5-base along with parameter-efficient Low-Rank Adaptation (LoRA) modules to support ten Indian languages. For each clinical dialogue, the model produces (1) a free-text English summary, (2) an English structured key–value (KnV) JSON summary, and (3) QA responses in the dialogue’s original language. We conducted preprocessing, fine-tuning, and inference, and evaluated across QA, textual, and structured metrics, analyzing performance in low-resource settings. The adapter weights, tokenizer, and inference scripts are publicly released to promote transparency and reproducibility.