2025
ACL
ACL 2025
ArgHiTZ at ArchEHR-QA 2025: A Two-Step Divide and Conquer Approach to Patient Question Answering for Top Factuality
Abstract
AbstractThis work presents three different approaches to address the ArchEHR-QA 2025 Shared Task on automated patient question answering. We introduce an end-to-end prompt-based baseline and two two-step methods to divide the task, without utilizing any external knowledge. Both two step approaches first extract essential sentences from the clinical text—by prompt or similarity ranking—, and then generate the final answer from these notes. Results indicate that the re-ranker based two-step system performs best, highlighting the importance of selecting the right approach for each subtask. Our best run achieved an overall score of 0.44, ranking 8th out of 30 on the leaderboard, securing the top position in overall factuality.
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Keyword Pioneer
— sentence extraction
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Natural Language Processing, Reinforcement Learning, Speech & Audio
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Interdisciplinary Bridge
— Healthcare & Medicine and Machine Learning and Natural Language Processing
Authors
Topics
Natural Language Processing > Applications > Machine Reading Comprehension
Natural Language Processing > Applications > Question Answering
Healthcare & Medicine > Clinical > Clinical NLP
Machine Learning > Core Methods > Multi-Task Learning
Healthcare & Medicine > Clinical > Medical AI
Machine Learning > Learning Types > Prompt Engineering